From ed4caa98b152169663fe59f0963f1bbf70189e7d Mon Sep 17 00:00:00 2001 From: Dennis Wei Date: Thu, 21 Aug 2025 16:02:51 -0700 Subject: [PATCH 1/5] MExGenExplainer base class for common methods (__init__, segment_input) Signed-off-by: Dennis Wei --- icx360/algorithms/mexgen/__init__.py | 1 + icx360/algorithms/mexgen/clime.py | 50 +------------ icx360/algorithms/mexgen/lshap.py | 51 +------------ icx360/algorithms/mexgen/mexgen.py | 107 +++++++++++++++++++++++++++ 4 files changed, 114 insertions(+), 95 deletions(-) create mode 100644 icx360/algorithms/mexgen/mexgen.py diff --git a/icx360/algorithms/mexgen/__init__.py b/icx360/algorithms/mexgen/__init__.py index adb92ff..c7d7c38 100644 --- a/icx360/algorithms/mexgen/__init__.py +++ b/icx360/algorithms/mexgen/__init__.py @@ -4,3 +4,4 @@ from .clime import CLIME from .lshap import LSHAP +from .mexgen import MExGenExplainer diff --git a/icx360/algorithms/mexgen/clime.py b/icx360/algorithms/mexgen/clime.py index 23e9904..24a2961 100644 --- a/icx360/algorithms/mexgen/clime.py +++ b/icx360/algorithms/mexgen/clime.py @@ -12,13 +12,13 @@ import numpy as np from sklearn.linear_model import LinearRegression, lars_path -from icx360.algorithms.lbbe import LocalBBExplainer +from icx360.algorithms.mexgen import MExGenExplainer from icx360.utils.scalarizers import ProbScalarizedModel, TextScalarizedModel from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric from icx360.utils.subset_utils import mask_subsets, sample_subsets -class CLIME(LocalBBExplainer): +class CLIME(MExGenExplainer): """ MExGen C-LIME explainer @@ -31,40 +31,6 @@ class CLIME(LocalBBExplainer): "Scalarized model" that further wraps `model` with a method for computing scalar values based on the model's inputs or outputs. """ - def __init__(self, model, segmenter="en_core_web_trf", scalarizer="prob", **kwargs): - """ - Initialize MExGen C-LIME explainer. - - Args: - model (icx360.utils.model_wrappers.Model): - Model to explain, wrapped in an icx360.utils.model_wrappers.Model object. - segmenter (str): - Name of spaCy model to use in segmenter (icx360.utils.segmenters.SpaCySegmenter). - scalarizer (str): - Type of scalarizer to use. - "prob": probability of generating original output conditioned on perturbed inputs - (instantiates an icx360.utils.scalarizers.ProbScalarizedModel). - "text": similarity scores between original output and perturbed outputs - (instantiates an icx360.utils.scalarizers.TextScalarizedModel). - **kwargs (dict): - Additional keyword arguments for initializing scalarizer. - - Raises: - ValueError: If `scalarizer` is not "prob" or "text". - """ - self.model = model - - # Instantiate segmenter - self.segmenter = SpaCySegmenter(segmenter) - - # Instantiate scalarized model - if scalarizer == "prob": - self.scalarized_model = ProbScalarizedModel(model) - elif scalarizer == "text": - self.scalarized_model = TextScalarizedModel(model, **kwargs) - else: - raise ValueError("Scalarizer not supported") - def explain_instance(self, input_orig, unit_types="p", ind_segment=True, segment_type="s", max_phrase_length=10, model_params={}, scalarize_params={}, oversampling_factor=10, max_units_replace=2, empty_subset=True, replacement_str="", num_nonzeros=None, debias=True): @@ -126,19 +92,9 @@ def explain_instance(self, input_orig, unit_types="p", ind_segment=True, segment One or more sets of attribution scores (labelled by the type of scalarizer). """ # 1) Segment input text if needed - if type(ind_segment) is bool: - ind_segment = [ind_segment] - if type(input_orig) is str or any(ind_segment): - # Call segmenter - input_orig, unit_types, _ = self.segmenter.segment_units(input_orig, ind_segment, unit_types, segment_type=segment_type, max_phrase_length=max_phrase_length) - # Exclude units without alphanumeric characters from perturbation - unit_types = exclude_non_alphanumeric(unit_types, input_orig) + input_orig, unit_types = self.segment_input(input_orig, unit_types, ind_segment, segment_type, max_phrase_length) num_units = len(input_orig) - if type(unit_types) is str: - # Expand to list - unit_types = [unit_types] * num_units - # 2) Generate output for original input output_orig = self.model.generate([input_orig], text_only=False, **model_params) diff --git a/icx360/algorithms/mexgen/lshap.py b/icx360/algorithms/mexgen/lshap.py index de8caea..87a0396 100644 --- a/icx360/algorithms/mexgen/lshap.py +++ b/icx360/algorithms/mexgen/lshap.py @@ -13,13 +13,12 @@ import numpy as np -from icx360.algorithms.lbbe import LocalBBExplainer +from icx360.algorithms.mexgen import MExGenExplainer from icx360.utils.scalarizers import ProbScalarizedModel, TextScalarizedModel -from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric from icx360.utils.subset_utils import mask_subsets, sample_subsets -class LSHAP(LocalBBExplainer): +class LSHAP(MExGenExplainer): """ MExGen L-SHAP explainer @@ -32,40 +31,6 @@ class LSHAP(LocalBBExplainer): "Scalarized model" that further wraps `model` with a method for computing scalar values based on the model's inputs or outputs. """ - def __init__(self, model, segmenter="en_core_web_trf", scalarizer="prob", **kwargs): - """ - Initialize MExGen L-SHAP explainer. - - Args: - model (icx360.utils.model_wrappers.Model): - Model to explain, wrapped in an icx360.utils.model_wrappers.Model object. - segmenter (str): - Name of spaCy model to use in segmenter (icx360.utils.segmenters.SpaCySegmenter). - scalarizer (str): - Type of scalarizer to use. - "prob": probability of generating original output conditioned on perturbed inputs - (instantiates an icx360.utils.scalarizers.ProbScalarizedModel). - "text": similarity scores between original output and perturbed outputs - (instantiates an icx360.utils.scalarizers.TextScalarizedModel). - **kwargs (dict): - Additional keyword arguments for initializing scalarizer. - - Raises: - ValueError: If `scalarizer` is not "prob" or "text". - """ - self.model = model - - # Instantiate segmenter - self.segmenter = SpaCySegmenter(segmenter) - - # Instantiate scalarized model - if scalarizer == "prob": - self.scalarized_model = ProbScalarizedModel(model) - elif scalarizer == "text": - self.scalarized_model = TextScalarizedModel(model, **kwargs) - else: - raise ValueError("Scalarizer not supported") - def explain_instance(self, input_orig, unit_types="p", ind_interest=None, ind_segment=True, segment_type="s", max_phrase_length=10, model_params={}, scalarize_params={}, num_neighbors=2, max_units_replace=2, replacement_str=""): @@ -123,19 +88,9 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, ind_se One or more sets of attribution scores (labelled by the type of scalarizer). """ # 1) Segment input text if needed - if type(ind_segment) is bool: - ind_segment = [ind_segment] - if type(input_orig) is str or any(ind_segment): - # Call segmenter - input_orig, unit_types, _ = self.segmenter.segment_units(input_orig, ind_segment, unit_types, segment_type=segment_type, max_phrase_length=max_phrase_length) - # Exclude units without alphanumeric characters from perturbation - unit_types = exclude_non_alphanumeric(unit_types, input_orig) + input_orig, unit_types = self.segment_input(input_orig, unit_types, ind_segment, segment_type, max_phrase_length) num_units = len(input_orig) - # Expand to list if needed - if type(unit_types) is str: - unit_types = [unit_types] * num_units - if ind_interest is None: # Default is to attribute to all units that can be perturbed ind_interest = np.array(unit_types) != "n" diff --git a/icx360/algorithms/mexgen/mexgen.py b/icx360/algorithms/mexgen/mexgen.py new file mode 100644 index 0000000..305c99b --- /dev/null +++ b/icx360/algorithms/mexgen/mexgen.py @@ -0,0 +1,107 @@ +""" +Base class for MExGen explainers. + +The MExGen framework is described in: + Multi-Level Explanations for Generative Language Models. + Lucas Monteiro Paes and Dennis Wei et al. + The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). + https://arxiv.org/abs/2403.14459 +""" +# Assisted by watsonx Code Assistant in formatting and augmenting docstrings. + +from icx360.algorithms.lbbe import LocalBBExplainer +from icx360.utils.scalarizers import ProbScalarizedModel, TextScalarizedModel +from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric + + +class MExGenExplainer(LocalBBExplainer): + """ + Base class for MExGen explainers. + + Attributes: + model (icx360.utils.model_wrappers.Model): + Model to explain, wrapped in an icx360.utils.model_wrappers.Model object. + segmenter (icx360.utils.segmenters.SpaCySegmenter): + Object for segmenting input text into units using a spaCy model. + scalarized_model (icx360.utils.scalarizers.Scalarizer): + "Scalarized model" that further wraps `model` with a method for computing scalar values + based on the model's inputs or outputs. + """ + def __init__(self, model, segmenter="en_core_web_trf", scalarizer="prob", **kwargs): + """ + Initialize MExGen explainer. + + Args: + model (icx360.utils.model_wrappers.Model): + Model to explain, wrapped in an icx360.utils.model_wrappers.Model object. + segmenter (str): + Name of spaCy model to use in segmenter (icx360.utils.segmenters.SpaCySegmenter). + scalarizer (str): + Type of scalarizer to use. + "prob": probability of generating original output conditioned on perturbed inputs + (instantiates an icx360.utils.scalarizers.ProbScalarizedModel). + "text": similarity scores between original output and perturbed outputs + (instantiates an icx360.utils.scalarizers.TextScalarizedModel). + **kwargs (dict): + Additional keyword arguments for initializing scalarizer. + + Raises: + ValueError: If `scalarizer` is not "prob" or "text". + """ + self.model = model + + # Instantiate segmenter + self.segmenter = SpaCySegmenter(segmenter) + + # Instantiate scalarized model + if scalarizer == "prob": + self.scalarized_model = ProbScalarizedModel(model) + elif scalarizer == "text": + self.scalarized_model = TextScalarizedModel(model, **kwargs) + else: + raise ValueError("Scalarizer not supported") + + def segment_input(self, input_orig, unit_types="p", ind_segment=True, segment_type="s", max_phrase_length=10): + """ + Segment input text (if needed). + + Args: + input_orig (str or List[str]): + Input text as a single unit (if str) or segmented sequence of units (List[str]). + unit_types (str or List[str]): + Types of units in input_orig. + "p" for paragraph, "s" for sentence, "w" for word, + "n" for not to be perturbed/attributed to. + If str, applies to all units in input_orig, otherwise unit-specific. + ind_segment (bool or List[bool]): + Whether to segment input text. + If bool, applies to all units; if List[bool], applies to each unit individually. + segment_type (str): + Type of units to segment into: "s" for sentences, "w" for words, "ph" for phrases. + max_phrase_length (int): + Maximum phrase length in terms of spaCy tokens (default 10). + + Returns: + input_orig (List[str]): + Segmented input text. + unit_types (List[str]): + Updated types of units. + """ + # Convert ind_segment to list if needed + if type(ind_segment) is bool: + ind_segment = [ind_segment] + # Segment input text if needed + if type(input_orig) is str or any(ind_segment): + # Call segmenter + input_orig, unit_types, _ = self.segmenter.segment_units(input_orig, ind_segment, unit_types, + segment_type=segment_type, + max_phrase_length=max_phrase_length) + # Exclude units without alphanumeric characters from perturbation + unit_types = exclude_non_alphanumeric(unit_types, input_orig) + num_units = len(input_orig) + + # Expand to list if needed + if type(unit_types) is str: + unit_types = [unit_types] * num_units + + return input_orig, unit_types From e81fc77173926423edddb8439be3d22964ba4689 Mon Sep 17 00:00:00 2001 From: Dennis Wei Date: Fri, 22 Aug 2025 16:46:27 -0700 Subject: [PATCH 2/5] generate_or_wrap_output method to avoid re-generating output if provided Signed-off-by: Dennis Wei --- icx360/algorithms/mexgen/__init__.py | 2 +- icx360/algorithms/mexgen/clime.py | 14 ++++++---- icx360/algorithms/mexgen/lshap.py | 11 +++++--- icx360/algorithms/mexgen/mexgen.py | 40 ++++++++++++++++++++++++++++ 4 files changed, 57 insertions(+), 10 deletions(-) diff --git a/icx360/algorithms/mexgen/__init__.py b/icx360/algorithms/mexgen/__init__.py index c7d7c38..dd6bc70 100644 --- a/icx360/algorithms/mexgen/__init__.py +++ b/icx360/algorithms/mexgen/__init__.py @@ -2,6 +2,6 @@ Module containing submodules for MExGen C-LIME and MExGen L-SHAP explainers """ +from .mexgen import MExGenExplainer from .clime import CLIME from .lshap import LSHAP -from .mexgen import MExGenExplainer diff --git a/icx360/algorithms/mexgen/clime.py b/icx360/algorithms/mexgen/clime.py index 24a2961..f32b9d4 100644 --- a/icx360/algorithms/mexgen/clime.py +++ b/icx360/algorithms/mexgen/clime.py @@ -31,9 +31,11 @@ class CLIME(MExGenExplainer): "Scalarized model" that further wraps `model` with a method for computing scalar values based on the model's inputs or outputs. """ - def explain_instance(self, input_orig, unit_types="p", ind_segment=True, segment_type="s", max_phrase_length=10, - model_params={}, scalarize_params={}, oversampling_factor=10, max_units_replace=2, - empty_subset=True, replacement_str="", num_nonzeros=None, debias=True): + def explain_instance(self, input_orig, unit_types="p", output_orig=None, + ind_segment=True, segment_type="s", max_phrase_length=10, + model_params={}, scalarize_params={}, + oversampling_factor=10, max_units_replace=2, empty_subset=True, replacement_str="", + num_nonzeros=None, debias=True): """ Explain model output by attributing it to parts of the input text. @@ -48,6 +50,8 @@ def explain_instance(self, input_orig, unit_types="p", ind_segment=True, segment "p" for paragraph, "s" for sentence, "w" for word, "n" for not to be perturbed/attributed to. If str, applies to all units in input_orig, otherwise unit-specific. + output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): + [output] Output for original input if provided, otherwise None. ind_segment (bool or List[bool]): [segmentation] Whether to segment input text. If bool, applies to all units; if List[bool], applies to each unit individually. @@ -95,8 +99,8 @@ def explain_instance(self, input_orig, unit_types="p", ind_segment=True, segment input_orig, unit_types = self.segment_input(input_orig, unit_types, ind_segment, segment_type, max_phrase_length) num_units = len(input_orig) - # 2) Generate output for original input - output_orig = self.model.generate([input_orig], text_only=False, **model_params) + # 2) Generate output for original input or wrap provided output + output_orig = self.generate_or_wrap_output(input_orig, output_orig, model_params) # 3) Enumerate subsets of units that will be perturbed/replaced idx_replace = (np.array(unit_types) != "n").nonzero()[0] diff --git a/icx360/algorithms/mexgen/lshap.py b/icx360/algorithms/mexgen/lshap.py index 87a0396..fb01326 100644 --- a/icx360/algorithms/mexgen/lshap.py +++ b/icx360/algorithms/mexgen/lshap.py @@ -31,8 +31,9 @@ class LSHAP(MExGenExplainer): "Scalarized model" that further wraps `model` with a method for computing scalar values based on the model's inputs or outputs. """ - def explain_instance(self, input_orig, unit_types="p", ind_interest=None, ind_segment=True, segment_type="s", - max_phrase_length=10, model_params={}, scalarize_params={}, + def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output_orig=None, + ind_segment=True, segment_type="s", max_phrase_length=10, + model_params={}, scalarize_params={}, num_neighbors=2, max_units_replace=2, replacement_str=""): """ Explain model output by attributing it to parts of the input text. @@ -51,6 +52,8 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, ind_se ind_interest (bool or List[bool] or None): [input] Indicator of units to attribute to ("of interest"). Default None means np.array(unit_types) != "n". + output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): + [output] Output for original input if provided, otherwise None. ind_segment (bool or List[bool]): [segmentation] Whether to segment input text. If bool, applies to all units; if List[bool], applies to each unit individually. @@ -101,8 +104,8 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, ind_se idx_interest = ind_interest.nonzero()[0] idx_replace = (np.array(unit_types) != "n").nonzero()[0] - # 2) Generate output for original input - output_orig = self.model.generate([input_orig], text_only=False, **model_params) + # 2) Generate output for original input or wrap provided output + output_orig = self.generate_or_wrap_output(input_orig, output_orig, model_params) # 3) Initialize quantities # Initialize importance scores diff --git a/icx360/algorithms/mexgen/mexgen.py b/icx360/algorithms/mexgen/mexgen.py index 305c99b..e979bda 100644 --- a/icx360/algorithms/mexgen/mexgen.py +++ b/icx360/algorithms/mexgen/mexgen.py @@ -10,6 +10,7 @@ # Assisted by watsonx Code Assistant in formatting and augmenting docstrings. from icx360.algorithms.lbbe import LocalBBExplainer +from icx360.utils.model_wrappers import GeneratedOutput, HFModel from icx360.utils.scalarizers import ProbScalarizedModel, TextScalarizedModel from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric @@ -105,3 +106,42 @@ def segment_input(self, input_orig, unit_types="p", ind_segment=True, segment_ty unit_types = [unit_types] * num_units return input_orig, unit_types + + def generate_or_wrap_output(self, input_orig, output_orig=None, model_params={}): + """ + Generate output for original input or wrap provided output in a GeneratedOutput object + + Args: + input_orig (List[str]): + Original input segmented into units. + output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): + Output for original input if provided, otherwise None. + model_params (dict): + Additional keyword arguments for model generation (for the self.model.generate() method). + + Returns: + output_orig (icx360.utils.model_wrappers.GeneratedOutput): + Object containing output for original input. + + Raises: + TypeError: If `output_orig` is not str, List[str], GeneratedOutput, or None. + """ + if output_orig is None: + # Generate output for original input + output_orig = self.model.generate([input_orig], text_only=False, **model_params) + elif type(output_orig) in (str, list): + if type(output_orig) is str: + output_orig = [output_orig] + + # Wrap output text in a GeneratedOutput object + output_orig = GeneratedOutput(output_text=output_orig) + + if isinstance(self.model, HFModel) and isinstance(self.scalarized_model, ProbScalarizedModel): + # Also need output token IDs for ProbScalarizedModel with HFModel + output_orig.output_ids = self.model.convert_input(output_orig.output_text)["input_ids"] + output_orig.output_token_count = output_orig.output_ids.shape[1] + + elif not isinstance(output_orig, GeneratedOutput): + raise TypeError("output_orig must be a str, List[str], GeneratedOutput, or None.") + + return output_orig From 0a24bdd048e6d30013064de4c4dbc1b6fe4644bb Mon Sep 17 00:00:00 2001 From: Dennis Wei Date: Mon, 25 Aug 2025 18:11:15 -0400 Subject: [PATCH 3/5] update MExGen QA and RAG notebooks - generate output only once and always explain that output - visualize explanations as highlighted text Signed-off-by: Dennis Wei --- examples/mexgen/RAG.ipynb | 345 ++++++++++++++--------- examples/mexgen/question_answering.ipynb | 271 +++++++++++------- 2 files changed, 382 insertions(+), 234 deletions(-) diff --git a/examples/mexgen/RAG.ipynb b/examples/mexgen/RAG.ipynb index db703bd..446086e 100644 --- a/examples/mexgen/RAG.ipynb +++ b/examples/mexgen/RAG.ipynb @@ -13,7 +13,7 @@ "id": "4aa9ee79-c26a-430e-b9ba-8cc6757b9919", "metadata": {}, "source": [ - "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's response to a question in *retrieval-augmented generation* (RAG). The explanation shows which retrieved documents and which parts thereof are most important to the LLM.\n", + "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's response to a question in *retrieval-augmented generation* (RAG). The explanation shows which retrieved documents and which parts thereof are most important to the LLM. For more information on MExGen, please see the [paper](https://aclanthology.org/2025.acl-long.1553/).\n", "\n", "After setting things up in Section 1, we will obtain explanations in the form of document-level attributions to the retrieved documents in Section 2, followed by mixed document- and sentence-level attributions in Section 3. We will then evaluate the fidelity of these explanations to the LLM in Section 4." ] @@ -47,18 +47,11 @@ "execution_count": 1, "id": "3aac1b49-c977-4bd0-b5c1-d21e37588159", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/u/dwei/icx/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", "\n", "import matplotlib.pyplot as plt # for plotting perturbation curves\n", "import numpy as np\n", @@ -87,6 +80,7 @@ "source": [ "from icx360.algorithms.mexgen import CLIME, LSHAP # explainers\n", "from icx360.metrics import PerturbCurveEvaluator # fidelity evaluation\n", + "from icx360.utils.coloring_utils import color_units # highlight and display text\n", "from icx360.utils.general_utils import select_device # set device automatically\n", "from icx360.utils.model_wrappers import HFModel, VLLMModel # model wrappers" ] @@ -141,8 +135,8 @@ "metadata": {}, "outputs": [], "source": [ - "# model_type = \"flan-t5\"\n", - "model_type = \"granite-hf\"\n", + "model_type = \"flan-t5\"\n", + "# model_type = \"granite-hf\"\n", "# model_type = \"vllm\"" ] }, @@ -151,15 +145,7 @@ "execution_count": 5, "id": "96d1c5e0-8de8-401a-9eb4-35140d8af7b8", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading checkpoint shards: 100%|██████████████████████████████████████| 2/2 [00:07<00:00, 3.79s/it]\n" - ] - } - ], + "outputs": [], "source": [ "if model_type == \"flan-t5\":\n", " model_name = \"google/flan-t5-large\"\n", @@ -263,7 +249,9 @@ { "cell_type": "markdown", "id": "312de547-8570-44a2-a6f8-d784f88fdf41", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "### Option 2: Retrieve documents using a retrieval model" ] @@ -401,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "a2e067a2-7de0-4844-afe8-233b141afd19", "metadata": {}, "outputs": [ @@ -465,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "id": "a0cbec39-9d23-4236-9e2a-bcfdab1eeac8", "metadata": {}, "outputs": [], @@ -516,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "id": "87647ef2-4ee6-47f2-8aa1-f0db6ca0b511", "metadata": {}, "outputs": [], @@ -537,19 +525,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "id": "202f23b7-85b4-4e2b-b6e1-62009019b465", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'max_new_tokens': 64,\n", - " 'chat_template': True,\n", - " 'system_prompt': 'You are an AI assistant that provides a *very short* answer to the user *solely based on the provided documents*, aiming to answer the user as correctly as possible. If no documents are provided, answer from your memory.'}" + "{'max_new_tokens': 64}" ] }, - "execution_count": 17, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -582,17 +568,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "id": "8395fd7d-ee16-4106-abfd-4101ef8a05e1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[' The coldest state in the contiguous USA is Alaska.']" + "['idaho']" ] }, - "execution_count": 18, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -612,24 +598,29 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "id": "4d73858e-0d2f-493b-8186-e03ee133055f", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[' Alaska is the coldest state of the contiguous USA.']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "Token indices sequence length is longer than the specified maximum sequence length for this model (740 > 512). Running this sequence through the model will result in indexing errors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Alaska']\n" + ] } ], "source": [ "prompt, unit_types = apply_template_for_generation(documents, question)\n", - "wrapped_model.generate(\"\".join(prompt), **model_params)" + "output_orig = wrapped_model.generate(\"\".join(prompt), **model_params)\n", + "print(output_orig)" ] }, { @@ -658,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "id": "f330501a-98c8-44fd-aff4-5338bb4f5804", "metadata": {}, "outputs": [], @@ -682,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "id": "24efb978-6ae0-4cfc-bb26-bf45f98fce43", "metadata": {}, "outputs": [], @@ -703,16 +694,17 @@ "id": "7e5f79a4-a226-4130-8be5-d9ffb4ff799c", "metadata": {}, "source": [ - "We call the explainer's `explain_instance` method with the following parameters:\n", + "To explain the response generated above with retrieved documents, we call the explainer's `explain_instance` method with the following parameters:\n", "- `prompt`: This is a list of 7 prompt elements (\"units\"), returned by `apply_template_for_generation`. The first unit is part of the prompt template (the word \"Context:\" or \"Documents:\", and possibly an instruction), the middle 5 units are the retrieved documents, and the last unit is the question.\n", "- `unit_types`: Also a list of length 7, returned by `apply_template_for_generation`. The type corresponding to the documents is `\"p\"` (\"paragraph\"), and the type of the first and last units is `\"n\"` (\"not of interest\"). The explainer will thus attribute only to the documents.\n", + "- `output_orig`: The response generated above with retrieved documents\n", "- `ind_segment`: Also a list of 7 elements, all equal to `False`. This means that units will not be segmented into smaller units (for now) and an importance score will be computed for each document as a whole.\n", "- `model_params`: The desired model generation parameters from above" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "id": "cf78a208-6f84-42f2-8778-3e48c2e70d1c", "metadata": {}, "outputs": [], @@ -722,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "id": "891c5440-f287-44a4-a694-50a6ad8db96a", "metadata": {}, "outputs": [ @@ -735,7 +727,7 @@ } ], "source": [ - "output_dict_doc = explainer.explain_instance(prompt, unit_types, ind_segment=ind_segment, model_params=model_params)" + "output_dict_doc = explainer.explain_instance(prompt, unit_types, output_orig=output_orig, ind_segment=ind_segment, model_params=model_params)" ] }, { @@ -751,22 +743,22 @@ "id": "13458292-4553-4e63-8946-a5fba5f822f0", "metadata": {}, "source": [ - "The explainer returns a dictionary. The `\"output_orig\"` item shows the response for the original input." + "The explainer returns a dictionary. The `\"output_orig\"` item shows the response for the original input, i.e., the response generated above with retrieved documents." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "id": "68f6a63f-1d70-44bd-86cc-eb3d17b4a3ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[' Alaska is the coldest state of the contiguous USA.']" + "['Alaska']" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -780,12 +772,51 @@ "id": "4f5f9950-f365-43cd-93ed-a23cde55f7d9", "metadata": {}, "source": [ - "The `\"attributions\"` item is itself a dictionary, containing the 7 prompt units (`\"units\"`), the corresponding `\"unit_types\"`, and the importance scores, computed according to the `\"prob\"` scalarizer and non-zero only for the units of interest (the documents). These are displayed below as a pandas DataFrame." + "The `\"attributions\"` item is itself a dictionary, containing the 7 prompt units (`\"units\"`), the corresponding `\"unit_types\"`, and the importance scores, computed according to the `\"prob\"` scalarizer and non-zero only for the units of interest (the documents). These are displayed below, first as highlighted text, and then as a pandas DataFrame to show the numerical scores." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, + "id": "d1f5f3b5-5d1d-4d40-b4fb-f8b95512a010", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Answer the user Question using the following Context: \n", + "\n", + "Context:\n", + "\n", + " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", + "\n", + " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", + "\n", + " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", + "\n", + " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", + "\n", + " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", + "\n", + " \n", + "\n", + "Question: What is the coldest state of the contiguous USA?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_units(output_dict_doc[\"attributions\"][\"units\"], output_dict_doc[\"attributions\"][\"prob\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "id": "ee33b32e-d7f3-489c-834f-717ba0ebf8ab", "metadata": {}, "outputs": [ @@ -821,34 +852,34 @@ " \n", " \n", " \n", - " Documents: \\n\n", + " Answer the user Question using the following Context: \\n\\nContext:\\n\\n\n", " n\n", " -0.000000\n", " \n", " \n", - " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \\n\n", + " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \\n\\n\n", " p\n", - " 0.478495\n", + " 0.377750\n", " \n", " \n", - " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\n", + " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\\n\n", " p\n", - " 0.409607\n", + " -0.010540\n", " \n", " \n", - " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \\n\n", + " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \\n\\n\n", " p\n", - " 0.472641\n", + " 0.017820\n", " \n", " \n", - " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\n", + " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\\n\n", " p\n", - " 0.428662\n", + " -0.050607\n", " \n", " \n", - " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\n", + " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\\n\n", " p\n", - " 0.344068\n", + " -0.059520\n", " \n", " \n", " \\n\\nQuestion: What is the coldest state of the contiguous USA?\n", @@ -862,16 +893,16 @@ "text/plain": [ " unit_types prob\n", "units \n", - "Documents: \\n n -0.000000\n", - "Document 0: Alaska / Alaska Alaska (; ; ; ) is... p 0.478495\n", - "Document 1: Alaska / the 90s °F (the low-to-mi... p 0.409607\n", - "Document 2: Alaska / Southeast Alaska is a mid... p 0.472641\n", - "Document 3: Alaska / the action of the sea is ... p 0.428662\n", - "Document 4: Alaska / but is not connected by r... p 0.344068\n", + "Answer the user Question using the following Co... n -0.000000\n", + " Alaska / Alaska Alaska (; ; ; ) is a U.S. stat... p 0.377750\n", + " Alaska / the 90s °F (the low-to-mid 30s °C), w... p -0.010540\n", + " Alaska / Southeast Alaska is a mid-latitude oc... p 0.017820\n", + " Alaska / the action of the sea is directed\". A... p -0.050607\n", + " Alaska / but is not connected by road to the r... p -0.059520\n", "\\n\\nQuestion: What is the coldest state of the ... n -0.000000" ] }, - "execution_count": 25, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -914,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "id": "ec3db785-884a-47b9-bd13-7171a6e254a0", "metadata": {}, "outputs": [], @@ -929,6 +960,7 @@ "source": [ "The parameters for `explain_instance()` will be as follows:\n", "- `units` and `unit_types`: Take existing document-level units and unit types from `output_dict_doc[\"attributions\"]`\n", + "- `output_orig` same as before\n", "- `ind_segment`: We create a Boolean array that has value `True` in the positions corresponding to the top documents and `False` otherwise. This will tell the explainer to segment only the top documents.\n", "- `segment_type = \"s\"` for segmentation into sentences\n", "- `model_params` as before" @@ -936,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "id": "f26fbd16-47a1-4d33-9838-162aec1ac229", "metadata": {}, "outputs": [ @@ -946,7 +978,7 @@ "array([False, True, False, True, False, False, False])" ] }, - "execution_count": 27, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -979,7 +1011,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "id": "66f923cd-b153-465b-be9a-d785d2347df9", "metadata": {}, "outputs": [ @@ -987,14 +1019,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 92\n", - "toma_get_probs batch size = 64\n", - "toma_get_probs batch size = 28\n" + "toma_get_probs batch size = 92\n" ] } ], "source": [ - "output_dict_mixed = explainer.explain_instance(units, unit_types, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" + "output_dict_mixed = explainer.explain_instance(units, unit_types, output_orig=output_orig, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" ] }, { @@ -1015,7 +1045,46 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, + "id": "cd8c5a4c-df0e-40df-ad21-795047092209", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Answer the user Question using the following Context: \n", + "\n", + "Context:\n", + "\n", + " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", + "\n", + " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", + "\n", + " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", + "\n", + " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", + "\n", + " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", + "\n", + " \n", + "\n", + "Question: What is the coldest state of the contiguous USA?" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_units(output_dict_mixed[\"attributions\"][\"units\"], output_dict_mixed[\"attributions\"][\"prob\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "d6d0efc0-3105-4196-be20-028363c17eba", "metadata": {}, "outputs": [ @@ -1051,79 +1120,94 @@ " \n", " \n", " \n", - " Documents: \\n\n", + " Answer the user Question using the following Context: \\n\\nContext:\\n\\n\n", + " n\n", + " -0.000000\n", + " \n", + " \n", + " \n", " n\n", " -0.000000\n", " \n", " \n", - " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America.\n", + " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America.\n", " s\n", - " 0.468246\n", + " 0.625500\n", " \n", " \n", " The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait.\n", " s\n", - " 0.311257\n", + " 0.058528\n", " \n", " \n", " To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean.\n", " s\n", - " 0.183641\n", + " 0.047288\n", " \n", " \n", " The Pacific Ocean lies to the south and southwest.\n", " s\n", - " 0.149030\n", + " 0.004326\n", " \n", " \n", - " It is the largest state in the United States by area and the seventh largest subnational division in \\n\n", + " It is the largest state in the United States by area and the seventh largest subnational division in\n", " s\n", - " 0.142128\n", + " 0.074129\n", " \n", " \n", - " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\n", + " \\n\\n\n", + " n\n", + " -0.000000\n", + " \n", + " \n", + " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\\n\n", " p\n", - " 0.655111\n", + " 0.041008\n", + " \n", + " \n", + " \n", + " n\n", + " -0.000000\n", " \n", " \n", - " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts.\n", + " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts.\n", " s\n", - " 0.270497\n", + " 0.069577\n", " \n", " \n", " On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year.\n", " s\n", - " 0.376434\n", + " 0.022369\n", " \n", " \n", " Juneau averages over of precipitation a year, and Ketchikan averages over .\n", " s\n", - " 0.134923\n", + " 0.070767\n", " \n", " \n", " This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months.\n", " s\n", - " 0.194042\n", + " 0.006302\n", " \n", " \n", " The climate of Anchorage and south central Alaska is mild by Alaskan standards due\n", " s\n", - " 0.127792\n", + " 0.020253\n", " \n", " \n", - " \\n\n", + " \\n\\n\n", " n\n", " -0.000000\n", " \n", " \n", - " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\n", + " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\\n\n", " p\n", - " 0.803486\n", + " 0.071507\n", " \n", " \n", - " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\n", + " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\\n\n", " p\n", - " 0.810460\n", + " -0.010955\n", " \n", " \n", " \\n\\nQuestion: What is the coldest state of the contiguous USA?\n", @@ -1137,25 +1221,28 @@ "text/plain": [ " unit_types prob\n", "units \n", - "Documents: \\n n -0.000000\n", - "Document 0: Alaska / Alaska Alaska (; ; ; ) is... s 0.468246\n", - "The Canadian administrative divisions of Britis... s 0.311257\n", - "To the north are the Chukchi and Beaufort seas—... s 0.183641\n", - "The Pacific Ocean lies to the south and southwe... s 0.149030\n", - "It is the largest state in the United States by... s 0.142128\n", - "Document 1: Alaska / the 90s °F (the low-to-mi... p 0.655111\n", - "Document 2: Alaska / Southeast Alaska is a mid... s 0.270497\n", - "On an annual basis, Southeast is both the wette... s 0.376434\n", - "Juneau averages over of precipitation a year, a... s 0.134923\n", - "This is also the only region in Alaska in which... s 0.194042\n", - "The climate of Anchorage and south central Alas... s 0.127792\n", - " \\n n -0.000000\n", - "Document 3: Alaska / the action of the sea is ... p 0.803486\n", - "Document 4: Alaska / but is not connected by r... p 0.810460\n", + "Answer the user Question using the following Co... n -0.000000\n", + " n -0.000000\n", + "Alaska / Alaska Alaska (; ; ; ) is a U.S. state... s 0.625500\n", + "The Canadian administrative divisions of Britis... s 0.058528\n", + "To the north are the Chukchi and Beaufort seas—... s 0.047288\n", + "The Pacific Ocean lies to the south and southwe... s 0.004326\n", + "It is the largest state in the United States by... s 0.074129\n", + "\\n\\n n -0.000000\n", + " Alaska / the 90s °F (the low-to-mid 30s °C), w... p 0.041008\n", + " n -0.000000\n", + "Alaska / Southeast Alaska is a mid-latitude oce... s 0.069577\n", + "On an annual basis, Southeast is both the wette... s 0.022369\n", + "Juneau averages over of precipitation a year, a... s 0.070767\n", + "This is also the only region in Alaska in which... s 0.006302\n", + "The climate of Anchorage and south central Alas... s 0.020253\n", + "\\n\\n n -0.000000\n", + " Alaska / the action of the sea is directed\". A... p 0.071507\n", + " Alaska / but is not connected by road to the r... p -0.010955\n", "\\n\\nQuestion: What is the coldest state of the ... n -0.000000" ] }, - "execution_count": 29, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1198,7 +1285,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "id": "2b93d4e2-0dc4-4b47-a0ff-645eecc79e66", "metadata": {}, "outputs": [], @@ -1236,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "id": "7be38a1f-e82e-44fb-8b1d-8f2d0ffb72ee", "metadata": {}, "outputs": [ @@ -1263,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "id": "8929cd16-ab0b-4bc1-9c10-24478ac93bc2", "metadata": {}, "outputs": [ @@ -1271,7 +1358,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 11\n" + "toma_get_probs batch size = 10\n" ] } ], @@ -1298,23 +1385,23 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "id": "ac205761-9914-499e-9422-43f750421cf6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1365,7 +1452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.12.5" } }, "nbformat": 4, diff --git a/examples/mexgen/question_answering.ipynb b/examples/mexgen/question_answering.ipynb index 1b1a83d..6bd0122 100644 --- a/examples/mexgen/question_answering.ipynb +++ b/examples/mexgen/question_answering.ipynb @@ -13,7 +13,7 @@ "id": "d210ed4d-98ae-4f0e-a0ff-b1607a57ee1c", "metadata": {}, "source": [ - "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's response to a question in terms of a document provided as context to the LLM.\n", + "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's response to a question in terms of a document provided as context to the LLM. For more information on MExGen, please see the [paper](https://aclanthology.org/2025.acl-long.1553/).\n", "\n", "After setting things up in Section 1, we will obtain explanations in the form of sentence-level attributions to the input document in Section 2, followed by mixed word- and sentence-level attributions in Section 3. We will then evaluate the fidelity of these explanations to the LLM in Section 4." ] @@ -44,11 +44,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b6d8dc9a-e287-4d7c-87ee-bac11414382c", "metadata": {}, "outputs": [], "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "import matplotlib.pyplot as plt # for plotting perturbation curves\n", "import numpy as np\n", "from openai import OpenAI # for VLLM QA model\n", @@ -74,6 +77,7 @@ "source": [ "from icx360.algorithms.mexgen import CLIME, LSHAP # explainers\n", "from icx360.metrics import PerturbCurveEvaluator # fidelity evaluation\n", + "from icx360.utils.coloring_utils import color_units # highlight and display text\n", "from icx360.utils.general_utils import select_device # set device automatically\n", "from icx360.utils.model_wrappers import HFModel, VLLMModel # model wrappers" ] @@ -458,9 +462,10 @@ "id": "bf50e186-0788-4d93-a5ea-243c5869ee85", "metadata": {}, "source": [ - "We call the explainer's `explain_instance` method with the following parameters:\n", + "To explain the response that was generated above, we call the explainer's `explain_instance` method with the following parameters:\n", "- `prompt`: Recall that this is a list of 5 prompt elements (\"units\")\n", "- `unit_types`: Also a list of length 5. We set the type corresponding to the document to `\"p\"` (\"paragraph\") and the type of other units to `\"n\"` (\"not of interest\"), which will cause the explainer to attribute only to the document.\n", + "- `output_orig`: The response generated above\n", "- `ind_segment`: Also a list of length 5. We set `ind_segment[1] = True` and `False` otherwise. This will segment the document into sentences and compute an importance score for each sentence.\n", "- `segment_type = \"s\"` for segmentation into sentences\n", "- `model_params`: The desired model generation parameters from above" @@ -484,13 +489,6 @@ "id": "b995db36-e174-4599-b810-38995ced7bde", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.48.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -500,7 +498,7 @@ } ], "source": [ - "output_dict_sent = explainer.explain_instance(prompt, unit_types, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" + "output_dict_sent = explainer.explain_instance(prompt, unit_types, output_orig=output_orig, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" ] }, { @@ -516,7 +514,7 @@ "id": "e0330b59-fbbb-4fb3-b50f-9694bef1da4e", "metadata": {}, "source": [ - "The explainer returns a dictionary. The `\"output_orig\"` item shows the response for the original input." + "The explainer returns a dictionary. The `\"output_orig\"` item shows the response for the original input, i.e., the response that was generated above." ] }, { @@ -545,12 +543,39 @@ "id": "92f297ab-f546-4880-a0a7-b81aacbe6802", "metadata": {}, "source": [ - "The `\"attributions\"` item is itself a dictionary, containing the sentences that the document has been split into along with the four original units (`\"units\"`), the corresponding `\"unit_types\"`, and the importance scores for the sentences, computed according to the `\"prob\"` scalarizer. These are displayed below as a pandas DataFrame." + "The `\"attributions\"` item is itself a dictionary, containing the sentences that the document has been split into along with the four original units (`\"units\"`), the corresponding `\"unit_types\"`, and the importance scores for the sentences, computed according to the `\"prob\"` scalarizer. These are displayed below, first as highlighted text, and then as a pandas DataFrame to show the numerical scores." ] }, { "cell_type": "code", "execution_count": 18, + "id": "8d3b268b-53eb-4fbd-ba4d-56831f22b350", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003, Beyonce starred opposite Cuba Gooding, Jr., in a musical comedy as Lilly, a single mother whom Gooding's character falls in love with. The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", + "\n", + "Question: What movie did Beyonce star in in 2003? \n", + "\n", + "Answer: " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_units(output_dict_sent[\"attributions\"][\"units\"], output_dict_sent[\"attributions\"][\"prob\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "id": "2fa24dc7-7bc3-4b88-9e94-76dde161d448", "metadata": {}, "outputs": [ @@ -593,32 +618,32 @@ " \n", " In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million.\n", " s\n", - " -0.303321\n", + " -0.467680\n", " \n", " \n", " Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium.\n", " s\n", - " -0.090525\n", + " 0.099590\n", " \n", " \n", " In 2003, Beyonce starred opposite Cuba Gooding, Jr., in a musical comedy as Lilly, a single mother whom Gooding's character falls in love with.\n", " s\n", - " 5.442492\n", + " 6.336826\n", " \n", " \n", " The film received mixed reviews from critics but grossed $30 million in the U.S.\n", " s\n", - " 0.024841\n", + " -0.202770\n", " \n", " \n", " Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film.\n", " s\n", - " 0.030438\n", + " 0.197400\n", " \n", " \n", " Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts.\n", " s\n", - " 0.049272\n", + " -0.021606\n", " \n", " \n", " \\n\\nQuestion:\n", @@ -643,18 +668,18 @@ " unit_types prob\n", "units \n", "Context: n 0.000000\n", - "In July 2002, Beyonce continued her acting care... s -0.303321\n", - "Beyonce released \"Work It Out\" as the lead sing... s -0.090525\n", - "In 2003, Beyonce starred opposite Cuba Gooding,... s 5.442492\n", - "The film received mixed reviews from critics bu... s 0.024841\n", - "Beyonce released \"Fighting Temptation\" as the l... s 0.030438\n", - "Another of Beyonce's contributions to the sound... s 0.049272\n", + "In July 2002, Beyonce continued her acting care... s -0.467680\n", + "Beyonce released \"Work It Out\" as the lead sing... s 0.099590\n", + "In 2003, Beyonce starred opposite Cuba Gooding,... s 6.336826\n", + "The film received mixed reviews from critics bu... s -0.202770\n", + "Beyonce released \"Fighting Temptation\" as the l... s 0.197400\n", + "Another of Beyonce's contributions to the sound... s -0.021606\n", "\\n\\nQuestion: n 0.000000\n", "What movie did Beyonce star in in 2003? n 0.000000\n", "\\n\\nAnswer: n 0.000000" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -697,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "1277194a-8d37-43b9-bcb1-fa688544c174", "metadata": {}, "outputs": [], @@ -712,6 +737,7 @@ "source": [ "The parameters for `explain_instance()` will be as follows:\n", "- `units` and `unit_types`: Take existing sentence-level units and unit types from `output_dict_sent[\"attributions\"]`\n", + "- `output_orig` same as before\n", "- `ind_segment`: We create a Boolean array that has value `True` in the position corresponding to the top sentence and `False` otherwise. This will tell the explainer to segment only the top sentence.\n", "- `segment_type = \"w\"` for segmentation into words\n", "- `model_params` as before" @@ -719,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "8081f2d7-43af-4221-80c0-fe1ce59a002a", "metadata": {}, "outputs": [ @@ -730,7 +756,7 @@ " False])" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -763,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "de98a7b3-1f7f-4b53-9741-50528c4fe1a1", "metadata": {}, "outputs": [ @@ -776,7 +802,7 @@ } ], "source": [ - "output_dict_mixed = explainer.explain_instance(units, unit_types, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" + "output_dict_mixed = explainer.explain_instance(units, unit_types, output_orig=output_orig, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" ] }, { @@ -797,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "04a6efba-5ad4-4fb5-9e7c-280e199f346e", "metadata": {}, "outputs": [ @@ -807,7 +833,7 @@ "['musical comedy']" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -826,7 +852,34 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, + "id": "bcfc2128-8ae8-4215-9b44-2de0b801f4e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003 , Beyonce starred opposite Cuba Gooding , Jr. , in a musical comedy as Lilly , a single mother whom Gooding 's character falls in love with . The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", + "\n", + "Question: What movie did Beyonce star in in 2003? \n", + "\n", + "Answer: " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_units(output_dict_mixed[\"attributions\"][\"units\"], output_dict_mixed[\"attributions\"][\"prob\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "id": "79240cb3-06a7-4b6b-b31c-bebe5ceb06ce", "metadata": {}, "outputs": [ @@ -869,22 +922,22 @@ " \n", " In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million.\n", " s\n", - " -0.030021\n", + " -0.233123\n", " \n", " \n", " Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium.\n", " s\n", - " 0.085883\n", + " 0.320631\n", " \n", " \n", " In\n", " w\n", - " 0.091946\n", + " -0.052855\n", " \n", " \n", " 2003\n", " w\n", - " 1.777418\n", + " 0.045652\n", " \n", " \n", " ,\n", @@ -894,27 +947,27 @@ " \n", " Beyonce\n", " w\n", - " 0.143749\n", + " -0.084887\n", " \n", " \n", " starred\n", " w\n", - " 0.203287\n", + " -0.034127\n", " \n", " \n", " opposite\n", " w\n", - " 0.015318\n", + " -0.236251\n", " \n", " \n", " Cuba\n", " w\n", - " 0.023138\n", + " 0.078328\n", " \n", " \n", " Gooding\n", " w\n", - " 0.310289\n", + " 0.216411\n", " \n", " \n", " ,\n", @@ -924,7 +977,7 @@ " \n", " Jr.\n", " w\n", - " 0.025616\n", + " 0.022668\n", " \n", " \n", " ,\n", @@ -934,32 +987,32 @@ " \n", " in\n", " w\n", - " 0.034352\n", + " 0.471464\n", " \n", " \n", " a\n", " w\n", - " 0.021783\n", + " -0.190492\n", " \n", " \n", " musical\n", " w\n", - " 2.658460\n", + " 6.583380\n", " \n", " \n", " comedy\n", " w\n", - " 3.001379\n", + " -1.930192\n", " \n", " \n", " as\n", " w\n", - " 1.735643\n", + " 0.956929\n", " \n", " \n", " Lilly\n", " w\n", - " -0.632696\n", + " -0.279934\n", " \n", " \n", " ,\n", @@ -969,57 +1022,57 @@ " \n", " a\n", " w\n", - " -0.069837\n", + " -0.081362\n", " \n", " \n", " single\n", " w\n", - " -0.020159\n", + " -0.014852\n", " \n", " \n", " mother\n", " w\n", - " 0.060091\n", + " 0.129198\n", " \n", " \n", " whom\n", " w\n", - " 0.016289\n", + " 0.116678\n", " \n", " \n", " Gooding\n", " w\n", - " 0.010448\n", + " 0.075750\n", " \n", " \n", " 's\n", " w\n", - " 0.017195\n", + " 0.212793\n", " \n", " \n", " character\n", " w\n", - " -0.010148\n", + " 0.066415\n", " \n", " \n", " falls\n", " w\n", - " -0.004816\n", + " -0.131812\n", " \n", " \n", " in\n", " w\n", - " -0.005142\n", + " -0.056521\n", " \n", " \n", " love\n", " w\n", - " 0.009327\n", + " 0.015170\n", " \n", " \n", " with\n", " w\n", - " -0.004349\n", + " -0.189587\n", " \n", " \n", " .\n", @@ -1029,17 +1082,17 @@ " \n", " The film received mixed reviews from critics but grossed $30 million in the U.S.\n", " s\n", - " -0.005575\n", + " 0.058848\n", " \n", " \n", " Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film.\n", " s\n", - " 0.037958\n", + " 0.456628\n", " \n", " \n", " Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts.\n", " s\n", - " 0.083051\n", + " -0.202139\n", " \n", " \n", " \\n\\nQuestion:\n", @@ -1064,47 +1117,47 @@ " unit_types prob\n", "units \n", "Context: n 0.000000\n", - "In July 2002, Beyonce continued her acting care... s -0.030021\n", - "Beyonce released \"Work It Out\" as the lead sing... s 0.085883\n", - "In w 0.091946\n", - "2003 w 1.777418\n", + "In July 2002, Beyonce continued her acting care... s -0.233123\n", + "Beyonce released \"Work It Out\" as the lead sing... s 0.320631\n", + "In w -0.052855\n", + "2003 w 0.045652\n", ", n 0.000000\n", - "Beyonce w 0.143749\n", - "starred w 0.203287\n", - "opposite w 0.015318\n", - "Cuba w 0.023138\n", - "Gooding w 0.310289\n", + "Beyonce w -0.084887\n", + "starred w -0.034127\n", + "opposite w -0.236251\n", + "Cuba w 0.078328\n", + "Gooding w 0.216411\n", ", n 0.000000\n", - "Jr. w 0.025616\n", + "Jr. w 0.022668\n", ", n 0.000000\n", - "in w 0.034352\n", - "a w 0.021783\n", - "musical w 2.658460\n", - "comedy w 3.001379\n", - "as w 1.735643\n", - "Lilly w -0.632696\n", + "in w 0.471464\n", + "a w -0.190492\n", + "musical w 6.583380\n", + "comedy w -1.930192\n", + "as w 0.956929\n", + "Lilly w -0.279934\n", ", n 0.000000\n", - "a w -0.069837\n", - "single w -0.020159\n", - "mother w 0.060091\n", - "whom w 0.016289\n", - "Gooding w 0.010448\n", - "'s w 0.017195\n", - "character w -0.010148\n", - "falls w -0.004816\n", - "in w -0.005142\n", - "love w 0.009327\n", - "with w -0.004349\n", + "a w -0.081362\n", + "single w -0.014852\n", + "mother w 0.129198\n", + "whom w 0.116678\n", + "Gooding w 0.075750\n", + "'s w 0.212793\n", + "character w 0.066415\n", + "falls w -0.131812\n", + "in w -0.056521\n", + "love w 0.015170\n", + "with w -0.189587\n", ". n 0.000000\n", - "The film received mixed reviews from critics bu... s -0.005575\n", - "Beyonce released \"Fighting Temptation\" as the l... s 0.037958\n", - "Another of Beyonce's contributions to the sound... s 0.083051\n", + "The film received mixed reviews from critics bu... s 0.058848\n", + "Beyonce released \"Fighting Temptation\" as the l... s 0.456628\n", + "Another of Beyonce's contributions to the sound... s -0.202139\n", "\\n\\nQuestion: n 0.000000\n", "What movie did Beyonce star in in 2003? n 0.000000\n", "\\n\\nAnswer: n 0.000000" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1147,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "6a33894f-5b64-444d-915b-a64432dcf647", "metadata": {}, "outputs": [], @@ -1177,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "9a0ef9b0-77ec-4bd4-b46b-a4c69973f332", "metadata": {}, "outputs": [ @@ -1185,7 +1238,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 5\n" + "toma_get_probs batch size = 4\n" ] } ], @@ -1196,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "05ad7b91-9de6-40a6-a777-16747207eb2a", "metadata": {}, "outputs": [ @@ -1204,7 +1257,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 22\n" + "toma_get_probs batch size = 17\n" ] } ], @@ -1231,23 +1284,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "id": "2a3d6537-0b37-4b70-982f-3f21003aa2b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1273,6 +1326,14 @@ "\n", "For this example, the perturbation curve for mixed-level attributions rises more quickly from zero, indicating that mixed-level can identify the most important finer-grained units (i.e., words). However, sentence-level attributions may achieve a slightly larger decrease in log probability at higher perturbed fractions. Your results may vary however." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b90fb1cf-a52e-49c2-b708-a0ef9cbc3851", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1291,7 +1352,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.5" } }, "nbformat": 4, From efa59a73973a98bce482ec7ce3f9dfa6beae52a2 Mon Sep 17 00:00:00 2001 From: Dennis Wei Date: Mon, 25 Aug 2025 15:15:28 -0700 Subject: [PATCH 4/5] also include output token IDs for HFModel and TextScalarizedModel Signed-off-by: Dennis Wei --- icx360/algorithms/mexgen/mexgen.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/icx360/algorithms/mexgen/mexgen.py b/icx360/algorithms/mexgen/mexgen.py index e979bda..c656389 100644 --- a/icx360/algorithms/mexgen/mexgen.py +++ b/icx360/algorithms/mexgen/mexgen.py @@ -136,8 +136,8 @@ def generate_or_wrap_output(self, input_orig, output_orig=None, model_params={}) # Wrap output text in a GeneratedOutput object output_orig = GeneratedOutput(output_text=output_orig) - if isinstance(self.model, HFModel) and isinstance(self.scalarized_model, ProbScalarizedModel): - # Also need output token IDs for ProbScalarizedModel with HFModel + if isinstance(self.model, HFModel): + # Also include output token IDs for HFModel output_orig.output_ids = self.model.convert_input(output_orig.output_text)["input_ids"] output_orig.output_token_count = output_orig.output_ids.shape[1] From cfbcd78b28de067669f0d2f3cc292faa0733f23a Mon Sep 17 00:00:00 2001 From: Dennis Wei Date: Mon, 25 Aug 2025 19:15:17 -0400 Subject: [PATCH 5/5] update MExGen summarization notebook - generate output only once and always explain that output - visualize explanations as highlighted text Signed-off-by: Dennis Wei --- examples/mexgen/summarization.ipynb | 1045 ++++++++++++++------------- 1 file changed, 524 insertions(+), 521 deletions(-) diff --git a/examples/mexgen/summarization.ipynb b/examples/mexgen/summarization.ipynb index cd26384..e98df7c 100644 --- a/examples/mexgen/summarization.ipynb +++ b/examples/mexgen/summarization.ipynb @@ -13,7 +13,7 @@ "id": "69f4e228-1353-4b70-bed7-88caba364e22", "metadata": {}, "source": [ - "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's summarization of a document.\n", + "This notebook walks through an example of using MExGen (Multi-Level Explanations for Generative Language Models) to explain an LLM's summarization of a document. For more information on MExGen, please see the [paper](https://aclanthology.org/2025.acl-long.1553/).\n", "\n", "After setting things up in Section 1, we will obtain explanations in the form of sentence-level attributions to the input document in Section 2, followed by mixed phrase- and sentence-level attributions in Section 3. We will then evaluate the fidelity of these explanations to the LLM in Section 4." ] @@ -44,11 +44,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d4453f33-4b53-4c38-b80d-198d248955d7", "metadata": {}, "outputs": [], "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "from datasets import load_dataset # for XSum dataset\n", "import matplotlib.pyplot as plt # for plotting perturbation curves\n", "import numpy as np\n", @@ -75,6 +78,7 @@ "source": [ "from icx360.algorithms.mexgen import CLIME, LSHAP # explainers\n", "from icx360.metrics import PerturbCurveEvaluator # fidelity evaluation\n", + "from icx360.utils.coloring_utils import color_units # highlight and display text\n", "from icx360.utils.general_utils import select_device # set device automatically\n", "from icx360.utils.model_wrappers import HFModel, VLLMModel # model wrappers" ] @@ -129,8 +133,8 @@ "metadata": {}, "outputs": [], "source": [ - "model_type = \"distilbart\"\n", - "# model_type = \"granite-hf\"\n", + "# model_type = \"distilbart\"\n", + "model_type = \"granite-hf\"\n", "# model_type = \"vllm\"" ] }, @@ -139,7 +143,15 @@ "execution_count": 5, "id": "de00c07c-10e9-4be7-8c3e-e30d85edd37f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████████████████████████████████| 2/2 [00:07<00:00, 3.79s/it]\n" + ] + } + ], "source": [ "if model_type == \"distilbart\":\n", " model_name = \"sshleifer/distilbart-xsum-12-6\"\n", @@ -293,7 +305,9 @@ { "data": { "text/plain": [ - "{'max_new_tokens': 100}" + "{'max_new_tokens': 100,\n", + " 'chat_template': True,\n", + " 'system_prompt': 'Summarize the following article in one sentence. Do not preface the summary with anything.'}" ] }, "execution_count": 9, @@ -334,7 +348,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits as it continues to expand its online presence.']\n" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n" ] } ], @@ -419,7 +433,7 @@ "id": "4e125100-4924-4eb2-a825-5bfea601e23f", "metadata": {}, "source": [ - "We call the explainer's `explain_instance` method on the input document, with the model generation parameters in `model_params` and default settings otherwise. This will segment the document into sentences and attribute an importance score to each sentence." + "To explain the summary that was generated above, we call the explainer's `explain_instance` method, passing to it the input document, summary `output_orig`, model generation parameters in `model_params`, and default settings otherwise. This will segment the document into sentences and attribute an importance score to each sentence." ] }, { @@ -432,7 +446,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_generate batch size = 132\n" + "toma_generate batch size = 132\n", + "toma_generate batch size = 128\n", + "toma_generate batch size = 64\n", + "toma_generate batch size = 64\n", + "toma_generate batch size = 4\n" ] }, { @@ -446,8 +464,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits, helped by a surge in sales.']\n", - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits, helped by a surge in sales.', 'Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits.', 'Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits.', 'Inditex, the owner of chains including Zara, Massimo Dutti and Pull&Bear, has reported a sharp rise in half-year profits.', 'Inditex, the owner of chains including Zara and Pull&Bear, has reported a sharp rise in half-year profits as it continues to expand its online presence.']\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the six months ending 31 July, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, under the strategic leadership of Chairman Pablo Isla who emphasized technological advancements\", ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the first half of its fiscal year, driven by a 6-8% expansion of new store space, significant online growth, and technological advancements like mobile payments and a unified InWallet app across all brands.', \" Inditex, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 40 countries, particularly benefiting from steady economic growth in Spain.\"]\n", "NLI scalarizer ref->gen\n", "toma_call batch size = 132\n", "NLI scalarizer gen->ref\n", @@ -471,7 +489,7 @@ } ], "source": [ - "output_dict_sent = explainer.explain_instance(document, model_params=model_params)" + "output_dict_sent = explainer.explain_instance(document, output_orig=output_orig, model_params=model_params)" ] }, { @@ -487,7 +505,7 @@ "id": "b4596ddf-fec0-46b0-924a-37ed5d1a6100", "metadata": {}, "source": [ - "The explainer returns a dictionary. The `\"output_orig\"` item shows the output summary for the original document." + "The explainer returns a dictionary. The `\"output_orig\"` item shows the output summary for the original document, i.e., the summary generated above." ] }, { @@ -499,7 +517,7 @@ { "data": { "text/plain": [ - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits, helped by a surge in sales.']" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']" ] }, "execution_count": 14, @@ -516,12 +534,64 @@ "id": "e5ee1221-28ae-45e2-96b6-ec1554383111", "metadata": {}, "source": [ - "The `\"attributions\"` item is itself a dictionary, containing the sentences (`\"units\"`) that the document has been split into, the corresponding `\"unit_types\"`, and the importance scores for the sentences, one score for each similarity metric included in the scalarizer (NLI, BERTScore, etc.). These are displayed below as a pandas DataFrame, where we also normalize each column of scores by the maximum score." + "The `\"attributions\"` item is itself a dictionary, containing the sentences (`\"units\"`) that the document has been split into, the corresponding `\"unit_types\"`, and the importance scores for the sentences, one score for each similarity metric included in the scalarizer (NLI, BERTScore, etc.). We organize these scores into a pandas DataFrame and display them first as highlighted text, where we sum the scores across similarity metrics to show a single version." ] }, { "cell_type": "code", "execution_count": 15, + "id": "f76122db-9032-46d6-8a56-cd3168e0aaac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year. \n", + "Sales jumped from €9.4bn to €10.5bn, an increase of 11%. \n", + "The group's clothes can now be bought online in around 40 countries, it said. \n", + "Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka. \n", + "How Zara's founder became the richest man in the world - for two days\n", + "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", + "It also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\". \n", + "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", + "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", + "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", + "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", + "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", + "\"So there is a big opportunity there for them to expand online into new territories.\" \n", + "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", + "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", + "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", + "In a call with analysts, it said it would open 6-8% of new store space over course of the year. \n", + " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "attrib_scores_df = pd.DataFrame(output_dict_sent[\"attributions\"]).set_index(\"units\")\n", + "score_labels = explainer.scalarized_model.sim_scores\n", + "attrib_scores_df = attrib_scores_df[[\"unit_types\"] + score_labels]\n", + "\n", + "color_units(output_dict_sent[\"attributions\"][\"units\"], attrib_scores_df[score_labels].sum(axis=1))" + ] + }, + { + "cell_type": "markdown", + "id": "a36b49be-7186-4fc6-843d-1809dc46cd23", + "metadata": {}, + "source": [ + "We then display the pandas DataFrame to show the numerical scores for all similarity metrics, where we also normalize each column of scores by the maximum score." + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "69ae663f-b77a-4fdc-8be5-12cc54e3b7ba", "metadata": {}, "outputs": [ @@ -567,146 +637,146 @@ " \n", " The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year.\n", " s\n", - " 0.683280\n", " 1.000000\n", - " 0.643647\n", " 1.000000\n", " 1.000000\n", + " -0.168489\n", + " -0.258453\n", " \n", " \n", " \\nSales jumped from €9.4bn to €10.5bn, an increase of 11%.\n", " s\n", - " 0.729922\n", - " 0.395132\n", - " 0.669389\n", - " 0.225816\n", - " 0.260140\n", + " 0.464572\n", + " 0.424606\n", + " 0.182319\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", " s\n", - " 0.390864\n", - " 0.042293\n", - " -0.167252\n", - " 0.047666\n", - " 0.048265\n", + " -0.678671\n", + " -0.371478\n", + " -0.077795\n", + " -0.848492\n", + " -0.902917\n", " \n", " \n", " \\nInditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka.\n", " s\n", - " 0.600581\n", - " 0.297317\n", - " 0.265977\n", - " 0.457829\n", - " 0.377207\n", + " 0.786595\n", + " 0.094391\n", + " 0.558204\n", + " -0.817181\n", + " -0.782678\n", " \n", " \n", " \\nHow Zara's founder became the richest man in the world - for two days\\nChairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period.\n", " s\n", - " 0.707665\n", - " 0.703973\n", - " 1.000000\n", - " 0.472878\n", - " 0.584450\n", + " 0.902068\n", + " 0.182539\n", + " 0.411495\n", + " -0.536248\n", + " -0.589693\n", " \n", " \n", " \\nIt also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\".\n", " s\n", - " 0.645685\n", - " 0.268591\n", - " 0.255037\n", - " 0.186310\n", - " 0.205321\n", + " 0.372554\n", + " -0.045436\n", + " 0.263193\n", + " -0.807955\n", + " -0.818814\n", " \n", " \n", " \\nThis will encompass online apps for all of its brands and a specific app for the whole group called InWallet.\n", " s\n", - " 0.548298\n", - " 0.159034\n", - " 0.105412\n", - " 0.048554\n", - " 0.039156\n", + " -0.301760\n", + " 0.040993\n", + " -0.066632\n", + " 0.469033\n", + " 0.533445\n", " \n", " \n", " \\nMr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\"\n", " s\n", - " 0.556377\n", - " 0.211338\n", - " 0.150247\n", - " 0.104636\n", - " 0.101121\n", + " 0.318680\n", + " -0.106296\n", + " 0.365367\n", + " -0.767757\n", + " -0.868582\n", " \n", " \n", " \\nTom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important.\n", " s\n", - " 0.676632\n", - " 0.300964\n", - " 0.224815\n", - " 0.336209\n", - " 0.342782\n", + " -0.882482\n", + " -0.320363\n", + " -0.308789\n", + " -0.816193\n", + " -0.824716\n", " \n", " \n", " \\n\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said.\n", " s\n", - " 1.000000\n", - " 0.329794\n", - " 0.332405\n", - " 0.303154\n", - " 0.287829\n", + " 0.936409\n", + " 0.039259\n", + " 0.392456\n", + " -0.575641\n", + " -0.552495\n", " \n", " \n", " \\n\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online.\n", " s\n", - " 0.218182\n", - " 0.039034\n", - " 0.007694\n", - " -0.009965\n", - " -0.013622\n", + " -0.113354\n", + " 0.076609\n", + " -0.241972\n", + " -0.023411\n", + " -0.005748\n", " \n", " \n", " \\n\"So there is a big opportunity there for them to expand online into new territories.\"\n", " s\n", - " 0.188577\n", - " 0.044537\n", - " 0.007384\n", - " -0.024480\n", - " -0.018063\n", + " 0.057621\n", + " 0.137399\n", + " -0.048943\n", + " 0.321005\n", + " 0.308903\n", " \n", " \n", " \\nThe company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales.\n", " s\n", - " 0.760705\n", - " 0.187366\n", - " -0.026861\n", - " 0.152611\n", - " 0.146245\n", + " 0.851514\n", + " 0.165589\n", + " 0.491583\n", + " 0.323525\n", + " 0.387338\n", " \n", " \n", " \\nThat country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency.\n", " s\n", - " 0.923588\n", - " 0.446365\n", - " 0.601362\n", - " 0.508205\n", - " 0.462474\n", + " -0.590795\n", + " -0.295202\n", + " -0.117127\n", + " -0.770218\n", + " -0.767762\n", " \n", " \n", " \\nAll of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries.\n", " s\n", - " 0.488930\n", - " 0.038490\n", - " -0.192519\n", - " 0.031038\n", - " 0.039291\n", + " 0.618455\n", + " 0.087252\n", + " 0.348640\n", + " 0.251070\n", + " 0.269188\n", " \n", " \n", " \\nIn a call with analysts, it said it would open 6-8% of new store space over course of the year.\n", " s\n", - " 0.890768\n", - " 0.493915\n", - " 0.632328\n", - " 0.366810\n", - " 0.398132\n", + " 0.568610\n", + " 0.155764\n", + " 0.315034\n", + " -0.179051\n", + " -0.138836\n", " \n", " \n", " \\n\n", @@ -720,11 +790,11 @@ " \n", " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year.\n", " s\n", - " 0.361864\n", - " -0.083896\n", - " -0.156779\n", - " -0.048950\n", - " -0.049222\n", + " -0.372237\n", + " 0.015519\n", + " -0.301576\n", + " 0.204164\n", + " 0.205304\n", " \n", " \n", "\n", @@ -733,78 +803,74 @@ "text/plain": [ " unit_types nli_logit \\\n", "units \n", - "The world's biggest clothing retailer posted ne... s 0.683280 \n", - "\\nSales jumped from €9.4bn to €10.5bn, an incre... s 0.729922 \n", - "\\nThe group's clothes can now be bought online ... s 0.390864 \n", - "\\nInditex operates eight brands in 90 countries... s 0.600581 \n", - "\\nHow Zara's founder became the richest man in ... s 0.707665 \n", - "\\nIt also launched mobile phone payment in all ... s 0.645685 \n", - "\\nThis will encompass online apps for all of it... s 0.548298 \n", - "\\nMr Isla said: \"Both our online and bricks-and... s 0.556377 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... s 0.676632 \n", - "\\n\"I expect over the years they may find they d... s 1.000000 \n", - "\\n\"And while Zara is available in many of the t... s 0.218182 \n", - 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"The firm's strong performance sets it apart fro... s 0.361864 \n", + "The firm's strong performance sets it apart fro... s -0.372237 \n", "\n", " bert st \\\n", "units \n", - "The world's biggest clothing retailer posted ne... 1.000000 0.643647 \n", - "\\nSales jumped from €9.4bn to €10.5bn, an incre... 0.395132 0.669389 \n", - "\\nThe group's clothes can now be bought online ... 0.042293 -0.167252 \n", - "\\nInditex operates eight brands in 90 countries... 0.297317 0.265977 \n", - "\\nHow Zara's founder became the richest man in ... 0.703973 1.000000 \n", - "\\nIt also launched mobile phone payment in all ... 0.268591 0.255037 \n", - "\\nThis will encompass online apps for all of it... 0.159034 0.105412 \n", - "\\nMr Isla said: \"Both our online and bricks-and... 0.211338 0.150247 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... 0.300964 0.224815 \n", - "\\n\"I expect over the years they may find they d... 0.329794 0.332405 \n", - "\\n\"And while Zara is available in many of the t... 0.039034 0.007694 \n", - "\\n\"So there is a big opportunity there for them... 0.044537 0.007384 \n", - "\\nThe company also said it had benefited from s... 0.187366 -0.026861 \n", - "\\nThat country's clothing market grew at an ave... 0.446365 0.601362 \n", - "\\nAll of the group's brands increased their int... 0.038490 -0.192519 \n", - "\\nIn a call with analysts, it said it would ope... 0.493915 0.632328 \n", + "The world's biggest clothing retailer posted ne... 1.000000 1.000000 \n", + "\\nSales jumped from €9.4bn to €10.5bn, an incre... 0.424606 0.182319 \n", + "\\nThe group's clothes can now be bought online ... -0.371478 -0.077795 \n", + "\\nInditex operates eight brands in 90 countries... 0.094391 0.558204 \n", + "\\nHow Zara's founder became the richest man in ... 0.182539 0.411495 \n", + "\\nIt also launched mobile phone payment in all ... -0.045436 0.263193 \n", + "\\nThis will encompass online apps for all of it... 0.040993 -0.066632 \n", + "\\nMr Isla said: \"Both our online and bricks-and... -0.106296 0.365367 \n", + "\\nTom Gadsby, an analyst at Liberum, said the f... -0.320363 -0.308789 \n", + "\\n\"I expect over the years they may find they d... 0.039259 0.392456 \n", + "\\n\"And while Zara is available in many of the t... 0.076609 -0.241972 \n", + "\\n\"So there is a big opportunity there for them... 0.137399 -0.048943 \n", + "\\nThe company also said it had benefited from s... 0.165589 0.491583 \n", + "\\nThat country's clothing market grew at an ave... -0.295202 -0.117127 \n", + "\\nAll of the group's brands increased their int... 0.087252 0.348640 \n", + "\\nIn a call with analysts, it said it would ope... 0.155764 0.315034 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... -0.083896 -0.156779 \n", + "The firm's strong performance sets it apart fro... 0.015519 -0.301576 \n", "\n", " summ bart \n", "units \n", - "The world's biggest clothing retailer posted ne... 1.000000 1.000000 \n", - "\\nSales jumped from €9.4bn to €10.5bn, an incre... 0.225816 0.260140 \n", - "\\nThe group's clothes can now be bought online ... 0.047666 0.048265 \n", - "\\nInditex operates eight brands in 90 countries... 0.457829 0.377207 \n", - "\\nHow Zara's founder became the richest man in ... 0.472878 0.584450 \n", - "\\nIt also launched mobile phone payment in all ... 0.186310 0.205321 \n", - "\\nThis will encompass online apps for all of it... 0.048554 0.039156 \n", - "\\nMr Isla said: \"Both our online and bricks-and... 0.104636 0.101121 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... 0.336209 0.342782 \n", - "\\n\"I expect over the years they may find they d... 0.303154 0.287829 \n", - "\\n\"And while Zara is available in many of the t... -0.009965 -0.013622 \n", - "\\n\"So there is a big opportunity there for them... -0.024480 -0.018063 \n", - "\\nThe company also said it had benefited from s... 0.152611 0.146245 \n", - "\\nThat country's clothing market grew at an ave... 0.508205 0.462474 \n", - "\\nAll of the group's brands increased their int... 0.031038 0.039291 \n", - "\\nIn a call with analysts, it said it would ope... 0.366810 0.398132 \n", + "The world's biggest clothing retailer posted ne... -0.168489 -0.258453 \n", + "\\nSales jumped from €9.4bn to €10.5bn, an incre... 1.000000 1.000000 \n", + "\\nThe group's clothes can now be bought online ... -0.848492 -0.902917 \n", + "\\nInditex operates eight brands in 90 countries... -0.817181 -0.782678 \n", + "\\nHow Zara's founder became the richest man in ... -0.536248 -0.589693 \n", + "\\nIt also launched mobile phone payment in all ... -0.807955 -0.818814 \n", + "\\nThis will encompass online apps for all of it... 0.469033 0.533445 \n", + "\\nMr Isla said: \"Both our online and bricks-and... -0.767757 -0.868582 \n", + "\\nTom Gadsby, an analyst at Liberum, said the f... -0.816193 -0.824716 \n", + "\\n\"I expect over the years they may find they d... -0.575641 -0.552495 \n", + "\\n\"And while Zara is available in many of the t... -0.023411 -0.005748 \n", + "\\n\"So there is a big opportunity there for them... 0.321005 0.308903 \n", + "\\nThe company also said it had benefited from s... 0.323525 0.387338 \n", + "\\nThat country's clothing market grew at an ave... -0.770218 -0.767762 \n", + "\\nAll of the group's brands increased their int... 0.251070 0.269188 \n", + "\\nIn a call with analysts, it said it would ope... -0.179051 -0.138836 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... -0.048950 -0.049222 " + "The firm's strong performance sets it apart fro... 0.204164 0.205304 " ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "attrib_scores_df = pd.DataFrame(output_dict_sent[\"attributions\"]).set_index(\"units\")\n", - "\n", - "score_labels = explainer.scalarized_model.sim_scores\n", - "attrib_scores_df = attrib_scores_df[[\"unit_types\"] + score_labels]\n", "attrib_scores_df[score_labels] /= attrib_scores_df[score_labels].max(axis=0)\n", "attrib_scores_df" ] @@ -851,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "b7b3d705-f25d-43f1-9f3a-05eb9450a9d7", "metadata": {}, "outputs": [], @@ -867,6 +933,7 @@ "source": [ "The parameters for `explain_instance()` will be as follows:\n", "- `units` and `unit_types`: Take existing sentence-level units and unit types from `output_dict_sent[\"attributions\"]`\n", + "- `output_orig` same as above\n", "- `ind_segment`: We create a Boolean array that has value `True` in positions corresponding to the top 2 sentences in terms of the sum of scores, and `False` otherwise. This will tell the explainer to segment only these 2 sentences.\n", "- `segment_type = \"ph\"` for segmentation into phrases\n", "- `model_params` as before" @@ -874,18 +941,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "5905a633-3f35-4996-92b4-885e96f72c72", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ True, False, False, False, True, False, False, False, False,\n", + "array([ True, True, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False, False])" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -923,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "4de116e9-9d81-480d-a160-e8240bd003cc", "metadata": {}, "outputs": [ @@ -931,22 +998,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "became advcl How Zara's founder became\n", - "expanded ccomp had expanded its online stores\n", - "toma_generate batch size = 258\n", - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits.']\n", - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits.', 'Inditex, the owner of chains including Zara, Pull&Bear and Massimo Dutti, has reported a sharp rise in profits.', 'Inditex, the owner of chains including Zara, Massimo Dutti and Pull&Bear, has reported a sharp rise in profits.', 'Inditex, the owner of chains including Zara, Pull&Bear and Massimo Dutti, has reported a sharp rise in half-year profits.', 'Inditex, the owner of chains including Zara, Massimo Dutti and Pull&Bear, has reported a sharp rise in profits.']\n", + "toma_generate batch size = 195\n", + "toma_generate batch size = 128\n", + "toma_generate batch size = 64\n", + "toma_generate batch size = 64\n", + "toma_generate batch size = 64\n", + "toma_generate batch size = 3\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as part of its technological drive led by Chairman Pablo Isla.\", ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the first half of its fiscal year, driven by a strong online drive, expansion into 11 new countries, and increased international presence of its brands, particularly in Spain, outpacing European rivals H&M and Next.', \" Zara's parent company, Inditex, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and open new stores, particularly in Spain, amid steady economic growth in\", \" Zara, the world's largest clothing retailer, reported net earnings of €1.26bn, with a 11% sales increase from €9.4bn to €10.5bn, driven by its robust online expansion and strong performance in Spain, as it continues to invest in technology, including mobile payments and a unified online platform for all brands.\"]\n", "NLI scalarizer ref->gen\n", - "toma_call batch size = 258\n", + "toma_call batch size = 195\n", "NLI scalarizer gen->ref\n", - "toma_call batch size = 258\n", + "toma_call batch size = 195\n", "summ scalarizer\n", - "toma_get_probs batch size = 258\n" + "toma_get_probs batch size = 195\n" ] } ], "source": [ - "output_dict_mixed = explainer.explain_instance(units, unit_types, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" + "output_dict_mixed = explainer.explain_instance(units, unit_types, output_orig=output_orig, ind_segment=ind_segment, segment_type=segment_type, model_params=model_params)" ] }, { @@ -967,17 +1037,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "5b0c7ec1-1912-486a-a1ca-6f276d9771e7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Inditex, the owner of Zara and Massimo Dutti, has reported a sharp rise in half-year profits.']" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -991,12 +1061,63 @@ "id": "9413cee6-4cf4-47d7-8521-4e99fa933e22", "metadata": {}, "source": [ - "Mixed phrase- and sentence-level importance scores using each similarity metric, again normalized by the maximum score:" + "Mixed phrase- and sentence-level importance scores, first summed across similarity metrics and visualized as highlighted text:" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, + "id": "8696baea-9e72-4621-af65-e12eb8e087e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year . \n", + " Sales jumped from €9.4bn to €10.5bn, an increase of 11% . \n", + "The group's clothes can now be bought online in around 40 countries, it said. \n", + "Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka. \n", + "How Zara's founder became the richest man in the world - for two days\n", + "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", + "It also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\". \n", + "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", + "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", + "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", + "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", + "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", + "\"So there is a big opportunity there for them to expand online into new territories.\" \n", + "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", + "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", + "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", + "In a call with analysts, it said it would open 6-8% of new store space over course of the year. \n", + " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "attrib_scores_df = pd.DataFrame(output_dict_mixed[\"attributions\"]).set_index(\"units\")\n", + "attrib_scores_df = attrib_scores_df[[\"unit_types\"] + score_labels]\n", + "\n", + "color_units(output_dict_mixed[\"attributions\"][\"units\"], attrib_scores_df[score_labels].sum(axis=1))" + ] + }, + { + "cell_type": "markdown", + "id": "52d431ad-0ea8-4038-a45a-c2614299c4e1", + "metadata": {}, + "source": [ + "Importance scores using each similarity metric, again normalized by the maximum score:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "id": "865d634f-a0af-433e-8f81-bb0db2bfcb15", "metadata": {}, "outputs": [ @@ -1042,38 +1163,38 @@ " \n", " The world's biggest clothing retailer\n", " nsubj\n", - " 0.761780\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", " 1.000000\n", + " 0.106474\n", + " 0.584114\n", + " -0.721895\n", + " -0.785352\n", " \n", " \n", " posted\n", " ROOT\n", - " -0.019273\n", - " 0.013167\n", - " 0.066859\n", - " -0.067879\n", - " -0.029003\n", + " -1.616351\n", + " -0.856041\n", + " 0.314072\n", + " -1.913796\n", + " -1.909047\n", " \n", " \n", " net earnings of €1.26bn (£1.1bn)\n", " dobj\n", - " 0.753891\n", - " 0.553393\n", - " 0.565972\n", - " 0.302390\n", - " 0.312603\n", + " 0.508601\n", + " 1.000000\n", + " 0.749068\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", " in the six months to 31 July\n", " prep\n", - " 0.355874\n", - " 0.643719\n", - " 0.249408\n", - " 0.394622\n", - " 0.349698\n", + " 0.776250\n", + " 0.430682\n", + " 0.058211\n", + " -0.268173\n", + " -0.162354\n", " \n", " \n", " -\n", @@ -1087,11 +1208,11 @@ " \n", " up 8% on the same period last year\n", " advmod\n", - " 0.413663\n", - " 0.335615\n", - " 0.193663\n", - " 0.002911\n", - " 0.023664\n", + " 0.360540\n", + " 0.201041\n", + " 0.540846\n", + " -0.142716\n", + " -0.140645\n", " \n", " \n", " .\n", @@ -1103,33 +1224,6 @@ " 0.000000\n", " \n", " \n", - " \\nSales jumped from €9.4bn to €10.5bn, an increase of 11%.\n", - " s\n", - " 0.798400\n", - " 0.397421\n", - " 0.532677\n", - " 0.133202\n", - " 0.118465\n", - " \n", - " \n", - " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", - " s\n", - " -0.145032\n", - " -0.080781\n", - " -0.008423\n", - " 0.045120\n", - " 0.048113\n", - " \n", - " \n", - " \\nInditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka.\n", - " s\n", - " 0.697173\n", - " 0.721930\n", - " 0.890838\n", - " 0.770730\n", - " 0.698585\n", - " \n", - " \n", " \\n\n", " n\n", " 0.000000\n", @@ -1139,79 +1233,43 @@ " 0.000000\n", " \n", " \n", - " How Zara's founder became\n", - " non-leaf\n", - " 1.000000\n", - " 0.656790\n", - " 0.699175\n", - " 0.463225\n", - " 0.462987\n", - " \n", - " \n", - " the richest man in the world\n", - " attr\n", - " 0.498924\n", - " 0.202898\n", - " 0.242346\n", - " 0.015518\n", - " 0.008883\n", + " Sales\n", + " nsubj\n", + " -0.274059\n", + " 0.325036\n", + " -0.224990\n", + " -0.028301\n", + " 0.077366\n", " \n", " \n", - " -\n", - " n\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " jumped\n", + " ROOT\n", + " -0.086474\n", + " 0.089315\n", + " -0.149052\n", + " 0.669745\n", + " 0.593084\n", " \n", " \n", - " for two days\n", + " from €9.4bn\n", " prep\n", - " -0.026446\n", - " -0.024018\n", - " -0.038698\n", - " -0.026014\n", - " -0.025854\n", - " \n", - " \n", - " \\n\n", - " n\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.510297\n", + " -0.184605\n", + " 0.098164\n", + " -0.127199\n", + " -0.015523\n", " \n", " \n", - " Chairman and chief executive Pablo Isla\n", - " nsubj\n", - " 0.559903\n", - " 0.245624\n", - " 0.083386\n", - " -0.035465\n", - " -0.035741\n", - " \n", - " \n", - " emphasised\n", - " ROOT\n", - " 0.732028\n", - " 0.316333\n", - " 0.097631\n", - " -0.086952\n", - " -0.076342\n", - " \n", - " \n", - " the firm's investment in technology\n", - " dobj\n", - " 0.571772\n", - " 0.279794\n", - " 0.081857\n", - " -0.061290\n", - " -0.055816\n", + " to €10.5bn, an increase of 11%\n", + " prep\n", + " -0.008494\n", + " 0.701980\n", + " 0.117139\n", + " 0.288908\n", + " 0.417433\n", " \n", " \n", - " ,\n", + " .\n", " n\n", " 0.000000\n", " 0.000000\n", @@ -1220,157 +1278,130 @@ " 0.000000\n", " \n", " \n", - " saying\n", - " non-leaf\n", - " -0.000191\n", - " 0.056358\n", - " 0.110401\n", - " 0.077796\n", - " 0.074199\n", - " \n", - " \n", - " the firm\n", - " nsubj\n", - " -0.024670\n", - " 0.022419\n", - " 0.045460\n", - " 0.059150\n", - " 0.060242\n", - " \n", - " \n", - " had expanded its online stores\n", - " non-leaf\n", - " 0.175321\n", - " 0.109267\n", - " 0.125397\n", - " 0.050718\n", - " 0.049953\n", - " \n", - " \n", - " to 11 new countries\n", - " prep\n", - " 0.036182\n", - " 0.020531\n", - " 0.010739\n", - " 0.021521\n", - " 0.018555\n", + " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", + " s\n", + " -0.365596\n", + " -0.656207\n", + " -0.087184\n", + " -0.224039\n", + " -0.182479\n", " \n", " \n", - " in the period\n", - " prep\n", - " 0.029960\n", - " 0.028356\n", - " 0.009137\n", - " 0.015272\n", - " 0.011095\n", + " \\nInditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka.\n", + " s\n", + " 0.510964\n", + " 0.058316\n", + " 1.000000\n", + " -1.156331\n", + " -1.039227\n", " \n", " \n", - " .\n", - " n\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " \\nHow Zara's founder became the richest man in the world - for two days\\nChairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period.\n", + " s\n", + " 0.571232\n", + " 0.403418\n", + " 0.757583\n", + " -0.673680\n", + " -0.686417\n", " \n", " \n", " \\nIt also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\".\n", " s\n", - " 0.830614\n", - " 0.437893\n", - " 0.425579\n", - " 0.277856\n", - " 0.275284\n", + " 0.237210\n", + " -0.097768\n", + " 0.484087\n", + " -1.038979\n", + " -0.975200\n", " \n", " \n", " \\nThis will encompass online apps for all of its brands and a specific app for the whole group called InWallet.\n", " s\n", - " 0.358946\n", - " 0.159930\n", - " 0.133401\n", - " 0.036306\n", - " 0.037782\n", + " -0.192134\n", + " 0.088210\n", + " -0.122555\n", + " 0.603147\n", + " 0.635328\n", " \n", " \n", " \\nMr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\"\n", " s\n", - " 0.198519\n", - " 0.126102\n", - " 0.094169\n", - " 0.038909\n", - " 0.043991\n", + " 0.192191\n", + " -0.214986\n", + " 0.664587\n", + " -0.920382\n", + " -0.972651\n", " \n", " \n", " \\nTom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important.\n", " s\n", - " 0.198426\n", - " 0.121622\n", - " 0.117407\n", - " 0.115574\n", - " 0.122302\n", + " -0.572602\n", + " -0.675622\n", + " -0.575378\n", + " -0.982668\n", + " -0.920406\n", " \n", " \n", " \\n\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said.\n", " s\n", - " 0.586592\n", - " 0.306874\n", - " 0.277640\n", - " 0.129380\n", - " 0.119601\n", + " 0.596223\n", + " 0.084480\n", + " 0.721840\n", + " -0.740238\n", + " -0.658017\n", " \n", " \n", " \\n\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online.\n", " s\n", - " 0.731454\n", - " 0.491775\n", - " 0.374773\n", - " 0.300718\n", - " 0.266648\n", + " -0.072174\n", + " 0.164850\n", + " -0.445056\n", + " -0.030105\n", + " -0.006846\n", " \n", " \n", " \\n\"So there is a big opportunity there for them to expand online into new territories.\"\n", " s\n", - " -0.120767\n", - " -0.060138\n", - " -0.037352\n", - " -0.055202\n", - " -0.061474\n", + " 0.036688\n", + " 0.295659\n", + " -0.090021\n", + " 0.412792\n", + " 0.367900\n", " \n", " \n", " \\nThe company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales.\n", " s\n", - " 0.587978\n", - " 0.278297\n", - " 0.253464\n", - " 0.189680\n", - " 0.189629\n", + " 0.542170\n", + " 0.356319\n", + " 0.904162\n", + " 0.416033\n", + " 0.461316\n", " \n", " \n", " \\nThat country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency.\n", " s\n", - " 0.774326\n", - " 0.406946\n", - " 0.414373\n", - " 0.272611\n", - " 0.258739\n", + " -0.375242\n", + " -0.632490\n", + " -0.215306\n", + " -0.980383\n", + " -0.902879\n", " \n", " \n", " \\nAll of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries.\n", " s\n", - " -0.933324\n", - " -0.441782\n", - " -0.337152\n", - " -0.193818\n", - " -0.188448\n", + " 0.393162\n", + " 0.185929\n", + " 0.641166\n", + " 0.316148\n", + " 0.312922\n", " \n", " \n", " \\nIn a call with analysts, it said it would open 6-8% of new store space over course of the year.\n", " s\n", - " 0.405407\n", - " 0.114183\n", - " 0.045864\n", - " -0.099814\n", - " -0.102668\n", + " 0.363273\n", + " 0.338824\n", + " 0.579605\n", + " -0.216823\n", + " -0.149992\n", " \n", " \n", " \\n\n", @@ -1384,11 +1415,11 @@ " \n", " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year.\n", " s\n", - " 0.573354\n", - " 0.203896\n", - " 0.106801\n", - " 0.053895\n", - " 0.050738\n", + " -0.237008\n", + " 0.033395\n", + " -0.554685\n", + " 0.262542\n", + " 0.244516\n", " \n", " \n", "\n", @@ -1397,139 +1428,107 @@ "text/plain": [ " unit_types nli_logit \\\n", "units \n", - "The world's biggest clothing retailer nsubj 0.761780 \n", - "posted ROOT -0.019273 \n", - "net earnings of €1.26bn (£1.1bn) dobj 0.753891 \n", - "in the six months to 31 July prep 0.355874 \n", + "The world's biggest clothing retailer nsubj 1.000000 \n", + "posted ROOT -1.616351 \n", + "net earnings of €1.26bn (£1.1bn) dobj 0.508601 \n", + "in the six months to 31 July prep 0.776250 \n", "- n 0.000000 \n", - "up 8% on the same period last year advmod 0.413663 \n", + "up 8% on the same period last year advmod 0.360540 \n", ". n 0.000000 \n", - "\\nSales jumped from €9.4bn to €10.5bn, an incre... s 0.798400 \n", - "\\nThe group's clothes can now be bought online ... s -0.145032 \n", - "\\nInditex operates eight brands in 90 countries... s 0.697173 \n", - "\\n n 0.000000 \n", - "How Zara's founder became non-leaf 1.000000 \n", - "the richest man in the world attr 0.498924 \n", - "- n 0.000000 \n", - "for two days prep -0.026446 \n", "\\n n 0.000000 \n", - "Chairman and chief executive Pablo Isla nsubj 0.559903 \n", - "emphasised ROOT 0.732028 \n", - "the firm's investment in technology dobj 0.571772 \n", - ", n 0.000000 \n", - "saying non-leaf -0.000191 \n", - "the firm nsubj -0.024670 \n", - "had expanded its online stores non-leaf 0.175321 \n", - "to 11 new countries prep 0.036182 \n", - "in the period prep 0.029960 \n", + "Sales nsubj -0.274059 \n", + "jumped ROOT -0.086474 \n", + "from €9.4bn prep 0.510297 \n", + "to €10.5bn, an increase of 11% prep -0.008494 \n", ". n 0.000000 \n", - "\\nIt also launched mobile phone payment in all ... s 0.830614 \n", - "\\nThis will encompass online apps for all of it... s 0.358946 \n", - "\\nMr Isla said: \"Both our online and bricks-and... s 0.198519 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... s 0.198426 \n", - "\\n\"I expect over the years they may find they d... s 0.586592 \n", - "\\n\"And while Zara is available in many of the t... s 0.731454 \n", - "\\n\"So there is a big opportunity there for them... s -0.120767 \n", - "\\nThe company also said it had benefited from s... s 0.587978 \n", - "\\nThat country's clothing market grew at an ave... s 0.774326 \n", - "\\nAll of the group's brands increased their int... s -0.933324 \n", - "\\nIn a call with analysts, it said it would ope... s 0.405407 \n", + "\\nThe group's clothes can now be bought online ... s -0.365596 \n", + "\\nInditex operates eight brands in 90 countries... s 0.510964 \n", + "\\nHow Zara's founder became the richest man in ... s 0.571232 \n", + "\\nIt also launched 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-0.038698 \n", "\\n 0.000000 0.000000 \n", - "Chairman and chief executive Pablo Isla 0.245624 0.083386 \n", - "emphasised 0.316333 0.097631 \n", - "the firm's investment in technology 0.279794 0.081857 \n", - ", 0.000000 0.000000 \n", - "saying 0.056358 0.110401 \n", - "the firm 0.022419 0.045460 \n", - "had expanded its online stores 0.109267 0.125397 \n", - "to 11 new countries 0.020531 0.010739 \n", - "in the period 0.028356 0.009137 \n", + "Sales 0.325036 -0.224990 \n", + "jumped 0.089315 -0.149052 \n", + "from €9.4bn -0.184605 0.098164 \n", + "to €10.5bn, an increase of 11% 0.701980 0.117139 \n", ". 0.000000 0.000000 \n", - "\\nIt also launched mobile phone payment in all ... 0.437893 0.425579 \n", - "\\nThis will encompass online apps for all of it... 0.159930 0.133401 \n", - "\\nMr Isla said: \"Both our online and bricks-and... 0.126102 0.094169 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... 0.121622 0.117407 \n", - "\\n\"I expect over the years they may find they 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analyst at Liberum, said the f... -0.675622 -0.575378 \n", + "\\n\"I expect over the years they may find they d... 0.084480 0.721840 \n", + "\\n\"And while Zara is available in many of the t... 0.164850 -0.445056 \n", + "\\n\"So there is a big opportunity there for them... 0.295659 -0.090021 \n", + "\\nThe company also said it had benefited from s... 0.356319 0.904162 \n", + "\\nThat country's clothing market grew at an ave... -0.632490 -0.215306 \n", + "\\nAll of the group's brands increased their int... 0.185929 0.641166 \n", + "\\nIn a call with analysts, it said it would ope... 0.338824 0.579605 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... 0.203896 0.106801 \n", + "The firm's strong performance sets it apart fro... 0.033395 -0.554685 \n", "\n", " summ bart \n", "units \n", - "The world's biggest clothing retailer 1.000000 1.000000 \n", - "posted -0.067879 -0.029003 \n", - "net earnings of €1.26bn (£1.1bn) 0.302390 0.312603 \n", - "in the six months to 31 July 0.394622 0.349698 \n", + "The world's biggest clothing retailer -0.721895 -0.785352 \n", + "posted -1.913796 -1.909047 \n", + "net earnings of €1.26bn (£1.1bn) 1.000000 1.000000 \n", + "in the six months to 31 July -0.268173 -0.162354 \n", "- 0.000000 0.000000 \n", - "up 8% on the same period last year 0.002911 0.023664 \n", + "up 8% on the same period last year -0.142716 -0.140645 \n", ". 0.000000 0.000000 \n", - "\\nSales jumped from €9.4bn to €10.5bn, an incre... 0.133202 0.118465 \n", - "\\nThe group's clothes can now be bought online ... 0.045120 0.048113 \n", - "\\nInditex operates eight brands in 90 countries... 0.770730 0.698585 \n", "\\n 0.000000 0.000000 \n", - "How Zara's founder became 0.463225 0.462987 \n", - "the richest man in the world 0.015518 0.008883 \n", - "- 0.000000 0.000000 \n", - "for two days -0.026014 -0.025854 \n", - "\\n 0.000000 0.000000 \n", - "Chairman and chief executive Pablo Isla -0.035465 -0.035741 \n", - "emphasised -0.086952 -0.076342 \n", - "the firm's investment in technology -0.061290 -0.055816 \n", - ", 0.000000 0.000000 \n", - "saying 0.077796 0.074199 \n", - "the firm 0.059150 0.060242 \n", - "had expanded its online stores 0.050718 0.049953 \n", - "to 11 new countries 0.021521 0.018555 \n", - "in the period 0.015272 0.011095 \n", + "Sales -0.028301 0.077366 \n", + "jumped 0.669745 0.593084 \n", + "from €9.4bn -0.127199 -0.015523 \n", + "to €10.5bn, an increase of 11% 0.288908 0.417433 \n", ". 0.000000 0.000000 \n", - "\\nIt also launched mobile phone payment in all ... 0.277856 0.275284 \n", - "\\nThis will encompass online apps for all of it... 0.036306 0.037782 \n", - "\\nMr Isla said: \"Both our online and bricks-and... 0.038909 0.043991 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... 0.115574 0.122302 \n", - "\\n\"I expect over the years they may find they d... 0.129380 0.119601 \n", - "\\n\"And while Zara is available in many of the t... 0.300718 0.266648 \n", - "\\n\"So there is a big opportunity there for them... -0.055202 -0.061474 \n", - "\\nThe company also said it had benefited from s... 0.189680 0.189629 \n", - "\\nThat country's clothing market grew at an ave... 0.272611 0.258739 \n", - "\\nAll of the group's brands increased their int... -0.193818 -0.188448 \n", - "\\nIn a call with analysts, it said it would ope... -0.099814 -0.102668 \n", + "\\nThe group's clothes can now be bought online ... -0.224039 -0.182479 \n", + "\\nInditex operates eight brands in 90 countries... -1.156331 -1.039227 \n", + "\\nHow Zara's founder became the richest man in ... -0.673680 -0.686417 \n", + "\\nIt also launched mobile phone payment in all ... -1.038979 -0.975200 \n", + "\\nThis will encompass online apps for all of it... 0.603147 0.635328 \n", + "\\nMr Isla said: \"Both our online and bricks-and... -0.920382 -0.972651 \n", + "\\nTom Gadsby, an analyst at Liberum, said the f... -0.982668 -0.920406 \n", + "\\n\"I expect over the years they may find they d... -0.740238 -0.658017 \n", + "\\n\"And while Zara is available in many of the t... -0.030105 -0.006846 \n", + "\\n\"So there is a big opportunity there for them... 0.412792 0.367900 \n", + "\\nThe company also said it had benefited from s... 0.416033 0.461316 \n", + "\\nThat country's clothing market grew at an ave... -0.980383 -0.902879 \n", + "\\nAll of the group's brands increased their int... 0.316148 0.312922 \n", + "\\nIn a call with analysts, it said it would ope... -0.216823 -0.149992 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... 0.053895 0.050738 " + "The firm's strong performance sets it apart fro... 0.262542 0.244516 " ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "attrib_scores_df = pd.DataFrame(output_dict_mixed[\"attributions\"]).set_index(\"units\")\n", - "attrib_scores_df = attrib_scores_df[[\"unit_types\"] + score_labels]\n", "attrib_scores_df[score_labels] /= attrib_scores_df[score_labels].max(axis=0)\n", "attrib_scores_df" ] @@ -1568,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "88d8f9df-41fd-44d1-86f2-e84664d3a726", "metadata": {}, "outputs": [], @@ -1598,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "6378374a-f738-42eb-8c60-0031a5b1dfd6", "metadata": {}, "outputs": [ @@ -1606,16 +1605,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 11\n", - "toma_get_probs batch size = 18\n", - "toma_get_probs batch size = 10\n", - "toma_get_probs batch size = 20\n", - "toma_get_probs batch size = 10\n", - "toma_get_probs batch size = 21\n", "toma_get_probs batch size = 9\n", - "toma_get_probs batch size = 18\n", + "toma_get_probs batch size = 13\n", + "toma_get_probs batch size = 10\n", + "toma_get_probs batch size = 15\n", "toma_get_probs batch size = 10\n", - "toma_get_probs batch size = 18\n" + "toma_get_probs batch size = 14\n", + "toma_get_probs batch size = 11\n", + "toma_get_probs batch size = 13\n", + "toma_get_probs batch size = 11\n", + "toma_get_probs batch size = 15\n" ] } ], @@ -1645,23 +1644,23 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "8b8a17a9-1908-4fa6-8cfa-489a86001db5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1692,14 +1691,18 @@ "id": "fd82d3ee-1809-4f01-b624-04189c994082", "metadata": {}, "source": [ - "In general, we are looking for perturbation curves to increase as more tokens are removed from the input. A higher perturbation curve is better because it indicates that the units identified by the explanation as important actually do have a larger effect on the LLM's output, and hence the explanation is more faithful to the LLM. Some observations for specific LLMs (your results may vary):\n", + "In general, we are looking for perturbation curves to increase as more tokens are removed from the input. A higher perturbation curve is better because it indicates that the units identified by the explanation as important actually do have a larger effect on the LLM's output, and hence the explanation is more faithful to the LLM. \n", "\n", - "**DistilBART**: For this model, mixed-level attribution scores (dashed curves) are generally more effective in identifying units whose removal causes larger drops in the model's log probability. \n", - "\n", - "**Granite-3.3-2B-Instruct**: Sentence-level attribution scores (solid curves) perform about as well as mixed-level scores for this model, and in some cases are better.\n", - "\n", - "**General observations**: There is no separation or clear ordering among the 5 similarity metrics, based on this single example. `\"summ\"`, and `\"bart\"` tend to be most similar to each other, and `\"bert\"` and `\"st\"` may be similar to each other as well." + "Some observations from this single example (your results may vary, and it is difficult to generalize from a single example): Sentence-level attribution scores (solid curves) perform about as well as mixed-level scores (dashed curves), and in some cases are better. There is no separation or clear ordering among the 5 similarity metrics. `\"summ\"`, and `\"bart\"` tend to be most similar to each other." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01d1aeed-ede0-4fbf-a98d-a208b8383ba7", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1718,7 +1721,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.12.5" } }, "nbformat": 4,