@inproceedings{tanaka-etal-2024-enhancing,
title = "Enhancing Consistency of Werewolf {AI} through Dialogue Summarization and Persona Information",
author = "Tanaka, Yoshiki and
Kaneko, Takumasa and
Onozeki, Hiroki and
Ezure, Natsumi and
Uehara, Ryuichi and
Qi, Zhiyang and
Higuchi, Tomoya and
Asahara, Ryutaro and
Inaba, Michimasa",
editor = "Kano, Yoshinobu",
booktitle = "Proceedings of the 2nd International AIWolfDial Workshop",
month = sep,
year = "2024",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.aiwolfdial-1.6",
pages = "48--57",
abstract = "The Werewolf Game is a communication game where players{'} reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent{'}s utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent{'}s utterances are contextually consistent and that the character, including tone, is maintained throughout the game.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tanaka-etal-2024-enhancing">
<titleInfo>
<title>Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yoshiki</namePart>
<namePart type="family">Tanaka</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Takumasa</namePart>
<namePart type="family">Kaneko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroki</namePart>
<namePart type="family">Onozeki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natsumi</namePart>
<namePart type="family">Ezure</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryuichi</namePart>
<namePart type="family">Uehara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyang</namePart>
<namePart type="family">Qi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomoya</namePart>
<namePart type="family">Higuchi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryutaro</namePart>
<namePart type="family">Asahara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michimasa</namePart>
<namePart type="family">Inaba</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd International AIWolfDial Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yoshinobu</namePart>
<namePart type="family">Kano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Tokyo, Japan</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The Werewolf Game is a communication game where players’ reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent’s utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent’s utterances are contextually consistent and that the character, including tone, is maintained throughout the game.</abstract>
<identifier type="citekey">tanaka-etal-2024-enhancing</identifier>
<location>
<url>https://aclanthology.org/2024.aiwolfdial-1.6</url>
</location>
<part>
<date>2024-09</date>
<extent unit="page">
<start>48</start>
<end>57</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information
%A Tanaka, Yoshiki
%A Kaneko, Takumasa
%A Onozeki, Hiroki
%A Ezure, Natsumi
%A Uehara, Ryuichi
%A Qi, Zhiyang
%A Higuchi, Tomoya
%A Asahara, Ryutaro
%A Inaba, Michimasa
%Y Kano, Yoshinobu
%S Proceedings of the 2nd International AIWolfDial Workshop
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F tanaka-etal-2024-enhancing
%X The Werewolf Game is a communication game where players’ reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent’s utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent’s utterances are contextually consistent and that the character, including tone, is maintained throughout the game.
%U https://aclanthology.org/2024.aiwolfdial-1.6
%P 48-57
Markdown (Informal)
[Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information](https://aclanthology.org/2024.aiwolfdial-1.6) (Tanaka et al., AIWolfDial-WS 2024)
ACL
- Yoshiki Tanaka, Takumasa Kaneko, Hiroki Onozeki, Natsumi Ezure, Ryuichi Uehara, Zhiyang Qi, Tomoya Higuchi, Ryutaro Asahara, and Michimasa Inaba. 2024. Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information. In Proceedings of the 2nd International AIWolfDial Workshop, pages 48–57, Tokyo, Japan. Association for Computational Linguistics.