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ProActLLM: Proactive Conversational Information Seeking with Large Language Models

ACM International Conference on Information and Knowledge Management (CIKM), pp. 6894–6897

Abstract

Large Language Models (LLMs) have transformed information access by enabling human-like text understanding and generation. This workshop explores the next step for conversational AI: building proactive information-seeking assistants that go beyond reactive question answering. We aim to investigate how LLMs can anticipate user needs, model complex context, support mixed-initiative interactions, integrate retrieval and external tools, personalize responses, adapt through feedback, and ensure fairness, transparency, and cognitive grounding. Bringing together experts from NLP, IR, HCI, and cognitive science, the workshop will serve as a timely forum for advancing intelligent, proactive dialogue systems. It will also foster interdisciplinary collaboration.

Authors 9

  1. Missouri University of Science and Technology

    Affiliation as printed

    Missouri University of Science and Technology, Rolla, MO, USA

  2. University of Sheffield

    Affiliation as printed

    University of Sheffield, London, United Kingdom

  3. Affiliation as printed

    Bloomberg, London, United Kingdom

  4. University of Guelph

    Affiliation as printed

    University of Guelph, Guelph, Canada

  5. Zhaochun Ren Aachen

    Leiden University

    Affiliation as printed

    Leiden University, Leiden, Netherlands

  6. University of Glasgow

    Affiliation as printed

    University of Glasgow, Glasgow, United Kingdom

  7. Dublin City University

    Affiliation as printed

    Dublin City University, Dublin, Ireland

  8. University College London

    Affiliation as printed

    University College London, London, United Kingdom

  9. University of Massachusetts Amherst

    Affiliation as printed

    University of Massachusetts, Amherst, WA, USA

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