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.
Recommended Citation
S. Chatterjee et al., "ProActLLM: Proactive Conversational Information Seeking with Large Language Models," Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 6894 - 6897, Association for computing Machinery, Nov 2025.
The definitive version is available at https://doi.org/10.1145/3746252.3761593
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
conversational information seeking; large language models; proactive conversational system
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery, All rights reserved.
Publication Date
10 Nov 2025
