答案格式变化对大型语言模型中性别偏见的影响
Effects of Answer Format Variation on Gender Bias in Large Language Models
- Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究探讨答案格式变化对LLMs性别偏见测量的影响，通过评估三种指令微调模型在不同格式下的表现，发现格式会显著改变测量结果，强调需将答案格式纳入LLM评估。
AI中文摘要：
大型语言模型（LLMs）中的性别偏见或其他社会偏见，常通过问答或调查基准进行评估，评估中要求LLM按预定义答案格式给出响应。调查科学中已知答案格式对回答有显著影响，正如LLM对提示措辞敏感一样。但据我们所知，尚未研究答案格式变化如何影响LLMs性别偏见的测量及其与人类响应分布的一致性。我们在相同条件下，于封闭式、李克特量表式和开放式三种格式中，评估了三个指令微调模型在BBQ基准和OpinionQA调查数据上的表现，比较不同格式下的偏见测量与分布一致性。我们发现答案格式会显著改变测量结果，包括排名顺序反转。这些差异源于每种格式引发不同的响应行为，如强制选择、基于量表的分布以及自由文本生成中的弃权（不执行）。我们的发现强调了将答案格式视为LLM评估重要组成部分的重要性，并推动多格式设计以实现更稳健的模型评估。
英文摘要：
Gender bias or other social biases in large language models (LLMs) are frequently evaluated with question answering or survey benchmarks where the LLM needs to give a response in a predefined answer format. It is well known in survey science that the answer format has a substantial impact on answers, just as LLMs are sensitive to the prompt wording. However, to our knowledge it has not been studied yet how changes in answer format impact the measurement of gender bias in LLMs and their alignment with human response distributions. We evaluate three instruction-tuned models on the BBQ benchmark and OpinionQA survey data across closed-ended, Likert-scaled and open-ended formats, comparing bias measurement and distributional alignment under otherwise identical conditions. We find that answer format does substantially alter measured outcomes, including reversals in order rankings. These differences arise because each format elicits distinct response behaviours, such as forced-choice selection, scale-based distributions and refusal in free-text generation. Our findings highlight the importance of treating answer format as a substantive component of LLM evaluation and motivate multi-format designs for more robust model assessment.
