Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO
Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like option positions and label symbols. Existing inference-time debiasing is costly and may harm reasoning, while pointwise training ignores that the same question should yield consistent answers across permutations. To address this issue, we propose Permutation-Aware Group Relative Policy Optimization (PA-GRPO), which mitigates selection bias by enforcing permutation-consistent semantic reasoning. PA-GRPO constructs a permutation group for each instance by generating multiple candidate permutations, and optimizes the model using two complementary m...
| Authors | Jinquan Zheng, Jia Yuan, Jiacheng Yao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 March 2026 |
| Last updated at source | 30 April 2026 |
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