检验RAG系统在性别、地域等敏感属性上的公平性问题
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems
- 设计针对RAG的场景化公平性评估框架
- 发现检索与生成阶段均存在公平性偏差
- 适合关注AI公平性的研究者和开发者
检索增强生成（RAG）近年来在开放域问答任务中受到广泛关注，因其能有效整合外部知识源。然而，其在性别、地理区域等敏感属性上的公平性问题尚不明确。一方面，语言模型在追求实用性（如提高精确匹配准确率）时可能忽视公平性；另一方面，RAG多组件架构复杂，各模块优化目标不同，难以识别和缓解偏见。本文提出一种专为RAG设计的公平性评估框架，通过场景化问题测试，分析不同人口属性下的表现差异。实验结果表明，尽管近期在提升性能方面取得进展，检索与生成阶段仍存在显著公平性问题。研究强调需在整个RAG流程中引入针对性干预措施。相关数据集与代码已公开于GitHub：https://github.com/elviswxy/RAG_fairness。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as language models evolve to prioritize utility, like improving exact match accuracy, fairness considerations may have been largely overlooked. Second, the complex, multi-component architecture of RAG methods poses challenges in identifying and mitigating biases, as each component is optimized for distinct objectives. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG, using scenario-based questions and analyzing disparities across demographic attributes. Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages. These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline. The dataset and code used in this study are publicly available at this GitHub Repository https://github.com/elviswxy/RAG_fairness .
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