RAG 是否在 LLM 中引入不公平性？评估检索增强生成系统中的公平性
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems
AI总结：
本文提出针对检索增强生成系统的公平性评估框架，实证发现公平性问题在检索和生成阶段依然存在，强调需全流程干预。
摘要：
检索增强生成（RAG）近年来因其在开放域问答（QA）任务中整合外部知识源的增强能力而受到广泛关注。然而，这些模型如何处理公平性问题仍不清楚，特别是涉及性别、地理位置和其他人口统计特征等敏感属性时。首先，随着语言模型优先考虑实用性（如提高精确匹配准确率），公平性考量可能在很大程度上被忽视。其次，RAG 方法复杂的多组件架构给识别和缓解偏见带来了挑战，因为每个组件都针对不同目标进行优化。在本文中，我们旨在实证评估多种 RAG 方法的公平性。我们提出了一种针对 RAG 量身定制的公平性评估框架，使用基于场景的问题并分析不同人口统计属性之间的差异。我们的实验结果表明，尽管近期在实用性驱动优化方面取得了进展，公平性问题在检索和生成阶段仍然存在。这些发现强调了在整个 RAG 流程中采取针对性干预措施以解决公平性问题的必要性。本研究所用的数据集和代码可在以下 GitHub 仓库公开获取：https://github.com/elviswxy/RAG_fairness。
英文摘要：
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 .
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。
