Multi-choice questions (MCQs) serve as a common yet important task format in the research of large language models (LLMs). Our work shows that LLMs exhibit an inherent "selection bias" in MCQs, which refers to LLMs' preferences to select options located at specific positions (like "Option C"). This bias is prevalent across various LLMs, making their performance vulnerable to option position changes in MCQs. We identify that one primary cause resulting in selection bias is option numbering, i.e., the ID symbols A/B/C/D associated with the options. To mitigate selection bias, we propose a new method called PriDe. PriDe first decomposes the observed model prediction distribution into an intrinsic prediction over option contents and a prior distribution over option IDs. It then estimates the prior by permutating option contents on a small number of test samples, which is used to debias the subsequent test samples. We demonstrate that, as a label-free, inference-time method, PriDe achieves a more effective and computation-efficient debiasing than strong baselines. We further show that the priors estimated by PriDe generalize well across different domains, highlighting its practical potential in broader scenarios.

通过对大型语言模型中多项选择题的研究，我们发现选项编号与选择偏见之间存在密切关系。为了减轻选择偏见，我们提出了一种名为PriDe的新方法，它通过计算先验分布来解决这一问题，该先验分布将选项内容与选项编号分离。PriDe方法不仅无需标签，而且在推断过程中具有更高的效果和计算效率，通过对不同领域的样本进行训练，我们证明了PriDe方法估计出的先验分布具有良好的泛化能力，在更广泛的场景中具有实用潜力。

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