ProActLLM: Proactive Conversational Information Seeking with Large Language Models
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
- Shubham Chatterjee (ORCID: https://orcid.org/0000-0002-6729-1346)
- Gareth J. F. Jones (ORCID: https://orcid.org/0000-0003-2923-8365)
- Xi Wang (ORCID: https://orcid.org/0000-0001-5936-9919)
- Debasis Ganguly (ORCID: https://orcid.org/0000-0003-0050-7138)
- Zhaochun Ren (ORCID: https://orcid.org/0000-0002-9076-6565)
- Shuo Zhang (ORCID: https://orcid.org/0000-0003-3179-4125)
- Hamed Zamani (ORCID: https://orcid.org/0000-0002-0800-3340)
- Emine Yilmaz (ORCID: https://orcid.org/0000-0003-4734-4532)
- Sajad Ebrahimi (ORCID: https://orcid.org/0009-0003-1630-3938)
Institutions
- Leiden University (NL)
- Missouri University of Science and Technology (US)
- University of Massachusetts Amherst (US)
- Dublin City University (IE)
- University College London (GB)
- University of Glasgow (GB)
- University of Guelph (CA)
- University of Sheffield (GB)
Publication Details
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1145/3746252.3761593
- Citations
- 1
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 2.48