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
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Missouri University of Science and Technology
Affiliation as printed
Missouri University of Science and Technology, Rolla, MO, USA
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University of Sheffield, London, United Kingdom
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Bloomberg, London, United Kingdom
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University of Guelph, Guelph, Canada
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Zhaochun Ren Aachen
Affiliation as printed
Leiden University, Leiden, Netherlands
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University of Glasgow, Glasgow, United Kingdom
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Affiliation as printed
Dublin City University, Dublin, Ireland
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Affiliation as printed
University College London, London, United Kingdom
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University of Massachusetts Amherst
Affiliation as printed
University of Massachusetts, Amherst, WA, USA
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