information-retrieval.html

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title: Information Retrieval
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                    <p class="text-center"> Our work brings together research expertise in information retrieval (IR),
                        recommender systems, natural language processing and machine learning. We focus on core IR
                        topics, from algorithms to applications.
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                <h4> Neural Information Retrieval </h4>

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                        Our is aim to create more efficient, effective, and interpretable neural models for information
                        retrieval. In our research on neural information retrieval, we are exploring the utility of
                        large language models to enhance retrieval and ranking algorithms. We focus mostly on the issues
                        of efficiency, including runtime and training efficiency, as well as explainability. To overcome
                        these challenges, we are actively investigating methods to improve the runtime performance of
                        large models, optimize training processes, and develop techniques for interpreting and
                        explaining the decisions made by neural retrieval systems.
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                <h4> Explainable IR </h4>
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                    <p>
                        Predictive models based on complex large language models are widely used in various domains like
                        search engines, recommender systems, health, legal, and finance. However, they often function as
                        black boxes, providing predictions or rankings without fully understanding how different factors
                        influence their outputs. Our research focuses on developing explainable AI techniques for
                        information retrieval tasks from various angles. We are researching interpretability approaches
                        to assess ranking models in web search, question-answering, and fact checking.
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                <h4> Complex Question Answering and Fact checking </h4>
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                    <p> Despite significant advancements in question answering brought about by large language models,
                        they still have limitations when it comes to certain types of questions. These include questions
                        involving numerical proficiency, compound questions, and complex reasoning, such as fact
                        verification across diverse sources. Our research is dedicated to comprehending these
                        limitations of large language models in complex question answering tasks. Moreover, we are
                        actively developing innovative approaches to address these challenges and make substantial
                        advancements in real-world question answering applications.
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                <h4> Children Information Retrieval </h4>
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                    <p>
                        IR technology embedded in our lives (Google or Amazon recommendations) responds to average
                        users. Children have particular cognitive, social, physical, and emotional needs that make the
                        information they seek, their experiences, sense making, and skills different from those of
                        adults. children are not simply short users; they are unique users. As such, they use IR tools
                        differently than adults. With research in this are we aim to empower children so they can
                        proficiently conduct information discovery tasks. This involved better modeling their needs and
                        expectations when interacting with IR systems, identifying challenges they face, and building
                        the algorithmic bridges needed to address them: from new ranking models and novel ways to
                        generate SERP to how to model.
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                <h4> Information Access for All </h4>
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                    <p> In our quest to access information, we regularly interact with search, recommender, and
                        question-answering systems. In theory, these systems serve a broad range of users in different
                        domains attempting to address different tasks. But how do they fare when faced with users,
                        contexts, and tasks they were not originally designed for? In this area of research, we focus on
                        identifying limitations inherent in non-traditional, i.e., non-expected, ecosystems and
                        designing the algorithms needed to address them. For this, we study how users interact with IR
                        systems using multiple lenses; we also identify how to leverage human traits for modeling users
                        and how that can impact IR technology design. Along the way, we question how we evaluate novel
                        solutions in this area, in the absence of benchmarks or given other existing restrictions, e.g.,
                        federal regulations and fairness objectives.
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            <h4 class="col-md-12 blog-post">Thesis topics</h4>
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                <li>Grounding fact-checking claims</li>
                <li>Blind spots in Large Language Models (LLMs)</li>
                <li>Personal question-answering using LLMs</li>
                <li>Information pollution detection</li>
                <li>Explaining personalized ranking</li>
                <li>Personalized ranking for non-traditional users</li>
                <li>Recommender systems and stereotype propagation</li>
                <li>The impact of readability on the web search process</li>
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            <h4 class="col-md-12 blog-post">Publications</h4>
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                    List of publications
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