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。