揭示大模型推荐中的偏见并提出有效缓解方法
Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness
- 分析多模型在音乐书籍推荐中的偏见机制
- 发现偏见随社会经济地位等因素加剧
- 通过提示工程与检索增强生成降低偏见
基于大语言模型（LLM）的推荐系统虽能深入分析内容与用户行为，提供全面建议，但常因训练数据偏差而偏好主流内容，忽视多样或非传统选项。本研究聚焦音乐、歌曲和书籍推荐中跨不同人口与文化群体的偏见问题，评估GPT、LLaMA和Gemini等多模型的表现，揭示偏见具有深层且广泛的影响。交叉身份与社会经济地位等上下文因素进一步放大偏见，使公平推荐更加困难。研究发现，尽管偏见根深蒂固，但简单的提示工程即可显著减轻其影响；本文还提出一种检索增强生成策略，更有效地缓解偏见。数值实验验证了偏见的普遍性及所提方法的有效性。
原文摘要 · Abstract (English)
Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diverse or non-traditional options. This study explores the interplay between bias and LLM-based recommendation systems, focusing on music, song, and book recommendations across diverse demographic and cultural groups. This paper analyzes bias in LLM-based recommendation systems across multiple models (GPT, LLaMA, and Gemini), revealing its deep and pervasive impact on outcomes. Intersecting identities and contextual factors, like socioeconomic status, further amplify biases, complicating fair recommendations across diverse groups. Our findings reveal that bias in these systems is deeply ingrained, yet even simple interventions like prompt engineering can significantly reduce it. We further propose a retrieval-augmented generation strategy to mitigate bias more effectively. Numerical experiments validate these strategies, demonstrating both the pervasive nature of bias and the impact of the proposed solutions.
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