Yuta Nakamura


pdf bib
AIWolfDial 2023: Summary of Natural Language Division of 5th International AIWolf Contest
Yoshinobu Kano | Neo Watanabe | Kaito Kagaminuma | Claus Aranha | Jaewon Lee | Benedek Hauer | Hisaichi Shibata | Soichiro Miki | Yuta Nakamura | Takuya Okubo | Soga Shigemura | Rei Ito | Kazuki Takashima | Tomoki Fukuda | Masahiro Wakutani | Tomoya Hatanaka | Mami Uchida | Mikio Abe | Akihiro Mikami | Takashi Otsuki | Zhiyang Qi | Kei Harada | Michimasa Inaba | Daisuke Katagami | Hirotaka Osawa | Fujio Toriumi
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges

We held our 5th 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 six Japanese speaking agents from five teams, and three English speaking agents, to mutually run games. By using the game logs, We performed human subjective evaluations 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. However, it is not perfect at all yet; the generated talks are sometimes inconsistent with the game actions, it is still doubtful that the agents could infer roles by logics rather than superficial utterance generations. It is not explicitly observed in this log but it would be still difficult to make an agent telling a lie, pretend as a villager but it has an opposite goal inside. 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.

pdf bib
Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models
Hitomi Yanaka | Yuta Nakamura | Yuki Chida | Tomoya Kurosawa
Proceedings of the 5th Clinical Natural Language Processing Workshop

Assessing the capacity of numerical understanding of vision-and-language models over images and texts is crucial for real vision-and-language applications, such as systems for automated medical image analysis. We provide a visual reasoning dataset focusing on numerical understanding in the medical domain. The experiments using our dataset show that current vision-and-language models fail to perform numerical inference in the medical domain. However, the data augmentation with only a small amount of our dataset improves the model performance, while maintaining the performance in the general domain.