TY - GEN
T1 - AIWolfDial 2024
T2 - 2nd Interenational AI Werewolf and Dialog System Workshop, AIWolfDial 2024, held in conjunction with the 17th International Natural Language Generation Conference, INLG 2024
AU - Kano, Yoshinobu
AU - Sahashi, Yuto
AU - Watanabe, Neo
AU - Kagaminuma, Kaito
AU - Aranha, Claus
AU - Katagami, Daisuke
AU - Harada, Kei
AU - Inaba, Michimasa
AU - Ito, Takeshi
AU - Osawa, Hirotaka
AU - Otsuki, Takashi
AU - Toriumi, Fujio
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - We held our 6th annual AIWolf international contest to automatically play the Werewolf game “Mafia”, where players try finding liars via conversations, aiming at promoting developments in creating agents of more natural conversations in higher level, such as longer contexts, personal relationships, semantics, pragmatics, and logics, revealing the capabilities and limits of the generative AIs. In our Natural Language Division of the contest, we had eight Japanese speaking agent teams, and five English speaking agents, to mutually run games. By using the game logs, we performed human subjective evaluations, win rates, and detailed log analysis. We found that the entire system performance has largely improved over the previous year, due to the recent advantages of the LLMs. There are several new ideas to improve the way using LLMs such as the summarization, characterization, and the logics outside LLMs, etc. However, it is not perfect at all yet; the generated talks are sometimes inconsistent with the game actions. Our future work includes to reveal the capability of the LLMs, whether they can make the duality of the “liar”, in other words, holding a “true” and a “false” circumstances of the agent at the same time, even holding what these circumstances look like from other agents.
AB - We held our 6th annual AIWolf international contest to automatically play the Werewolf game “Mafia”, where players try finding liars via conversations, aiming at promoting developments in creating agents of more natural conversations in higher level, such as longer contexts, personal relationships, semantics, pragmatics, and logics, revealing the capabilities and limits of the generative AIs. In our Natural Language Division of the contest, we had eight Japanese speaking agent teams, and five English speaking agents, to mutually run games. By using the game logs, we performed human subjective evaluations, win rates, and detailed log analysis. We found that the entire system performance has largely improved over the previous year, due to the recent advantages of the LLMs. There are several new ideas to improve the way using LLMs such as the summarization, characterization, and the logics outside LLMs, etc. However, it is not perfect at all yet; the generated talks are sometimes inconsistent with the game actions. Our future work includes to reveal the capability of the LLMs, whether they can make the duality of the “liar”, in other words, holding a “true” and a “false” circumstances of the agent at the same time, even holding what these circumstances look like from other agents.
UR - https://www.scopus.com/pages/publications/85216923854
UR - https://www.scopus.com/pages/publications/85216923854#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85216923854
T3 - AIWolfDial 2024 - 2nd International AIWolfDial Workshop, held in conjunction with the 17th International Natural Language Generation Conference - Proceedings of the Workshop
SP - 1
EP - 12
BT - AIWolfDial 2024 - 2nd International AIWolfDial Workshop, held in conjunction with the 17th International Natural Language Generation Conference - Proceedings of the Workshop
A2 - Kano, Yoshinobu
PB - Association for Computational Linguistics (ACL)
Y2 - 24 September 2024
ER -