Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912503714283520 |
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| author | Yanaka, Hitomi He, Xinqi Lu, Jie Han, Namgi Oh, Sunjin Kumon, Ryoma Matsuoka, Yuma Watabe, Katsuhiko Itatsu, Yuko |
| author_facet | Yanaka, Hitomi He, Xinqi Lu, Jie Han, Namgi Oh, Sunjin Kumon, Ryoma Matsuoka, Yuma Watabe, Katsuhiko Itatsu, Yuko |
| contents | An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective Yanaka, Hitomi He, Xinqi Lu, Jie Han, Namgi Oh, Sunjin Kumon, Ryoma Matsuoka, Yuma Watabe, Katsuhiko Itatsu, Yuko Computation and Language Artificial Intelligence An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes. |
| title | Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12327 |