Bias benchmarks are a popular method for studying the negative impacts of bias in LLMs, yet there has been little empirical investigation of whether these benchmarks are actually indicative of how real world harm may manifest in the real world. In this work, we study the correspondence between such decontextualized "trick tests" and evaluations that are more grounded in Realistic Use and Tangible {Effects (i.e. RUTEd evaluations). We explore this correlation in the context of gender-occupation bias--a popular genre of bias evaluation. We compare three de-contextualized evaluations adapted from the current literature to three analogous RUTEd evaluations applied to long-form content generation. We conduct each evaluation for seven instruction-tuned LLMs. For the RUTEd evaluations, we conduct repeated trials of three text generation tasks: children's bedtime stories, user personas, and English language learning exercises. We found no correspondence between trick tests and RUTEd evaluations. Specifically, selecting the least biased model based on the de-contextualized results coincides with selecting the model with the best performance on RUTEd evaluations only as often as random chance. We conclude that evaluations that are not based in realistic use are likely insufficient to mitigate and assess bias and real-world harms.

翻译：偏见基准测试是研究大语言模型偏见负面影响的流行方法，但关于这些基准测试能否真实反映现实世界中可能出现的危害，目前还缺乏实证研究。本文研究了这类脱离语境的“陷阱测试”与更贴近现实使用和具体影响的评估（即RUTEd评估）之间的对应关系。我们以性别-职业偏见这一常见的偏见评估类型为背景探索这种相关性。将现有文献中的三种脱离语境评估与三种应用于长篇内容生成的类比RUTEd评估进行比较。我们对七个指令微调大语言模型分别进行了每种评估。在RUTEd评估中，我们对三种文本生成任务（儿童睡前故事、用户画像和英语语言学习练习）进行了重复试验。结果发现，陷阱测试与RUTEd评估之间不存在对应关系。具体而言：基于脱离语境结果选择偏见最小的模型，其正好等同于选择在RUTEd评估中表现最佳的模型的概率仅与随机水平相当。我们得出结论：缺乏现实使用基础的评估可能不足以有效缓解和评估偏见及现实危害。