@inproceedings{li-etal-2025-towards-llm,
title = "Towards {LLM}-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair",
author = "Li, Junlin and
Bo, Peng and
Hsu, Yu-Yin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-short.1/",
doi = "10.18653/v1/2025.acl-short.1",
pages = "1--13",
ISBN = "979-8-89176-252-7",
abstract = "Grice{'}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."
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<abstract>Grice’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.</abstract>
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%0 Conference Proceedings
%T Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair
%A Li, Junlin
%A Bo, Peng
%A Hsu, Yu-Yin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F li-etal-2025-towards-llm
%X Grice’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.
%R 10.18653/v1/2025.acl-short.1
%U https://aclanthology.org/2025.acl-short.1/
%U https://doi.org/10.18653/v1/2025.acl-short.1
%P 1-13
Markdown (Informal)
[Towards LLM-powered Attentive Listener: A Pragmatic Approach through Quantity Self-Repair](https://aclanthology.org/2025.acl-short.1/) (Li et al., ACL 2025)
ACL