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    <abstract>We investigate intention detection in persuasive multi-turn dialogs employing the largest available Large Language Models (LLMs).Much of the prior research measures the intention detection capability of machine learning models without considering the conversational history.To evaluate LLMs’ intention detection capability in conversation, we modified the existing datasets of persuasive conversation and created datasets using a multiple-choice paradigm.It is crucial to consider others’ perspectives through their utterances when engaging in a persuasive conversation, especially when making a request or reply that is inconvenient for others.This feature makes the persuasive dialogue suitable for the dataset of measuring intention detection capability.We incorporate the concept of ‘face acts,’ which categorize how utterances affect mental states.This approach enables us to measure intention detection capability by focusing on crucial intentions and to conduct comprehensible analysis according to intention types.</abstract>
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%0 Conference Proceedings
%T Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues
%A Sakurai, Hiromasa
%A Miyao, Yusuke
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sakurai-miyao-2024-evaluating
%X We investigate intention detection in persuasive multi-turn dialogs employing the largest available Large Language Models (LLMs).Much of the prior research measures the intention detection capability of machine learning models without considering the conversational history.To evaluate LLMs’ intention detection capability in conversation, we modified the existing datasets of persuasive conversation and created datasets using a multiple-choice paradigm.It is crucial to consider others’ perspectives through their utterances when engaging in a persuasive conversation, especially when making a request or reply that is inconvenient for others.This feature makes the persuasive dialogue suitable for the dataset of measuring intention detection capability.We incorporate the concept of ‘face acts,’ which categorize how utterances affect mental states.This approach enables us to measure intention detection capability by focusing on crucial intentions and to conduct comprehensible analysis according to intention types.
%U https://aclanthology.org/2024.acl-long.90
%P 1635-1657

Markdown (Informal)

[Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues](https://aclanthology.org/2024.acl-long.90) (Sakurai & Miyao, ACL 2024)

• Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues (Sakurai & Miyao, ACL 2024)

ACL

• Hiromasa Sakurai and Yusuke Miyao. 2024. Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1635–1657, Bangkok, Thailand. Association for Computational Linguistics.