@inproceedings{8671b85d328a499d90e3e27d378db693,
title = "Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair",
abstract = "Grice{\textquoteright}s Quantity Maxims dictate that human speakers aim for the optimal quantity of information during conversation. To empower LLMs to self-repair their responses toward optimal quantity and improve their attentive listening skills, we propose Q-Tuning and Q-Traveling, which draw on heuristic path-finding to enable decoder-only LLMs to travel among multiple “Q-alternatives” (Quantity Alternatives) and search for the optimal quantity in coordination with a conversation goal. Automatic and human evaluations demonstrate the effectiveness of Q-Tuning and Q-Traveling in constructing human-like, user-centered conversation agents.",
author = "Junlin Li and Bo Peng and Hsu, \{Yu Yin\}",
note = "Publisher Copyright: {\textcopyright}2025 Association for Computational Linguistics.; The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) ; Conference date: 27-07-2025 Through 01-08-2025",
year = "2025",
month = jul,
doi = "10.18653/v1/2025.acl-short.1",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1--13",
editor = "Wanxiang Che and Joyce Nabende and Ekaterina Shutova and Pilehvar, \{Mohammad Taher\}",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
address = "United States",
}