A Japanese Benchmark for Evaluating Social Bias in Reasoning Based on Attribution Theory
In enhancing the fairness of Large Language Models (LLMs), evaluating social biases rooted in the cultural contexts of specific linguistic regions is essential. However, most existing Japanese benchmarks heavily rely on translating English data, which does not necessarily provide an evaluation suitable for Japanese culture. Furthermore, they only evaluate bias in the conclusion, failing to capture biases lurking in the reasoning. In this study, based on attribution theory in social psychology, we constructed a new dataset, ``JUBAKU-v2,'' which evaluates the bias in attributing behaviors to in-groups and out-groups within reasoning while fixing the conclusion. This dataset consists of 216 ...
| Authors | Taihei Shiotani, Masahiro Kaneko, Naoaki Okazaki |
|---|---|
| Primary category | cs.CL |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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