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        <title>BQA: Body Language Question Answering Dataset for Video Large Language Models</title>
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    <abstract>A large part of human communication relies on nonverbal cues such as facial expressions, eye contact, and body language. Unlike language or sign language, such nonverbal communication lacks formal rules, requiring complex reasoning based on commonsense understanding.Enabling current Video Large Language Models (VideoLLMs) to accurately interpret body language is a crucial challenge, as human unconscious actions can easily cause the model to misinterpret their intent.To address this, we propose a dataset, BQA, a body language question answering dataset, to validate whether the model can correctly interpret emotions from short clips of body language comprising 26 emotion labels of videos of body language.We evaluated various VideoLLMs on the BQA with and without Multimodal Chain of Thought (CoT) and revealed that understanding body language is challenging, and our analyses of the wrong answers by VideoLLMs show that certain VideoLLMs made largely biased answers depending on the age group and ethnicity of the individuals. We also found consistent error patterns in VideoLLMs.</abstract>
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%0 Conference Proceedings
%T BQA: Body Language Question Answering Dataset for Video Large Language Models
%A Ozaki, Shintaro
%A Hayashi, Kazuki
%A Oba, Miyu
%A Sakai, Yusuke
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%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 ozaki-etal-2025-bqa
%X A large part of human communication relies on nonverbal cues such as facial expressions, eye contact, and body language. Unlike language or sign language, such nonverbal communication lacks formal rules, requiring complex reasoning based on commonsense understanding.Enabling current Video Large Language Models (VideoLLMs) to accurately interpret body language is a crucial challenge, as human unconscious actions can easily cause the model to misinterpret their intent.To address this, we propose a dataset, BQA, a body language question answering dataset, to validate whether the model can correctly interpret emotions from short clips of body language comprising 26 emotion labels of videos of body language.We evaluated various VideoLLMs on the BQA with and without Multimodal Chain of Thought (CoT) and revealed that understanding body language is challenging, and our analyses of the wrong answers by VideoLLMs show that certain VideoLLMs made largely biased answers depending on the age group and ethnicity of the individuals. We also found consistent error patterns in VideoLLMs.
%R 10.18653/v1/2025.acl-short.10
%U https://aclanthology.org/2025.acl-short.10/
%U https://doi.org/10.18653/v1/2025.acl-short.10
%P 110-123

Markdown (Informal)

[BQA: Body Language Question Answering Dataset for Video Large Language Models](https://aclanthology.org/2025.acl-short.10/) (Ozaki et al., ACL 2025)

• BQA: Body Language Question Answering Dataset for Video Large Language Models (Ozaki et al., ACL 2025)

ACL

• Shintaro Ozaki, Kazuki Hayashi, Miyu Oba, Yusuke Sakai, Hidetaka Kamigaito, and Taro Watanabe. 2025. BQA: Body Language Question Answering Dataset for Video Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 110–123, Vienna, Austria. Association for Computational Linguistics.