揭示大语言模型的下游性别偏见：AI教育写作辅助研究
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
AI总结：
该研究针对AI教育写作辅助场景，通过大规模用户实验验证大语言模型的性别偏见不会传递到学生的写作产出中，为课堂应用AI写作辅助工具提供了乐观依据。
摘要：
大语言模型（LLM）正越来越多地被用于教育任务，例如为学生提供写作建议。尽管大语言模型潜力巨大，但众所周知它们存在固有偏见，可能会对学习者产生负面影响。以往的研究分别探究了模型和数据表征中的偏见，却忽略了大语言模型偏见对人类写作的潜在影响。在本文中，我们研究了偏见如何通过AI写作辅助流程传递。我们开展了一项大规模用户研究，共有231名学生用德语撰写商业案例同行评审。学生被分为五组，分别接受不同程度的写作辅助：一个课堂组获得基于特征的建议，另外四组从Prolific平台招募——其中一个对照组不接受任何辅助，两个组分别获得经过微调的GPT-2和GPT-3模型给出的建议，还有一个组获得预训练GPT-3.5给出的建议。我们使用GenBit性别偏见分析、词嵌入关联测试（WEAT）和句子嵌入关联测试（SEAT），在该流程的不同阶段评估性别偏见：模型嵌入层面、模型生成的建议层面以及学生撰写的评审内容层面。我们的结果表明，接受和未接受大语言模型建议的小组所产出的同行评审之间，在性别偏见程度上没有显著差异。因此，我们的研究对课堂中使用AI写作辅助持乐观态度，展现了大语言模型中的偏见不会传递到学生作答中的一种场景。
英文摘要：
Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. Despite their potential, LLMs are known to harbor inherent biases which may negatively impact learners. Previous studies have investigated bias in models and data representations separately, neglecting the potential impact of LLM bias on human writing. In this paper, we investigate how bias transfers through an AI writing support pipeline. We conduct a large-scale user study with 231 students writing business case peer reviews in German. Students are divided into five groups with different levels of writing support: one classroom group with feature-based suggestions and four groups recruited from Prolific -- a control group with no assistance, two groups with suggestions from fine-tuned GPT-2 and GPT-3 models, and one group with suggestions from pre-trained GPT-3.5. Using GenBit gender bias analysis, Word Embedding Association Tests (WEAT), and Sentence Embedding Association Test (SEAT) we evaluate the gender bias at various stages of the pipeline: in model embeddings, in suggestions generated by the models, and in reviews written by students. Our results demonstrate that there is no significant difference in gender bias between the resulting peer reviews of groups with and without LLM suggestions. Our research is therefore optimistic about the use of AI writing support in the classroom, showcasing a context where bias in LLMs does not transfer to students' responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
