迈向公平RAG：论公平排序在检索增强生成中的影响
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
AI总结：
本文首次系统评估了融合公平感知排序的RAG系统，在七个任务上实验十二个模型，证明公平检索能维持甚至提升排序与生成质量，并带来更均衡的来源归因，为构建公平RAG系统奠定基础。
摘要：
尽管检索在检索增强生成（RAG）系统中扮演核心角色，但现有关于RAG的研究大多忽视了已发展成熟的公平排序领域，也未能考虑所有相关利益方的权益。本文首次对融合公平感知排序的RAG系统进行系统性评估，同时关注排序公平性与归因公平性，后者确保生成内容中所引用来源获得公平的曝光。我们的评估聚焦于衡量项目侧公平性，具体而言是RAG系统检索到的相关项目所获得的公平曝光，并考察这种公平性如何影响系统有效性以及用户最终看到的生成输出中的来源归因。通过在七个不同任务上对十二个RAG模型进行实验，我们表明引入公平感知检索通常能够维持甚至提升排序质量和生成质量，反驳了公平性会损害系统性能的普遍看法。此外，我们证明公平检索实践能够在最终回答中带来更均衡的归因，确保生成器公平地引用其所依赖的来源。我们的发现强调了项目侧公平性在检索与生成中的重要性，为负责任且公平的RAG系统奠定了基础，并为未来公平排序与归因研究指明了方向。
英文摘要：
Despite the central role of retrieval in retrieval-augmented generation (RAG) systems, much of the existing research on RAG overlooks the well-established field of fair ranking and fails to account for the interests of all stakeholders involved. In this paper, we conduct the first systematic evaluation of RAG systems that integrate fairness-aware rankings, addressing both ranking fairness and attribution fairness, which ensures equitable exposure of the sources cited in the generated content. Our evaluation focuses on measuring item-side fairness, specifically the fair exposure of relevant items retrieved by RAG systems, and investigates how this fairness impacts both the effectiveness of the systems and the attribution of sources in the generated output that users ultimately see. By experimenting with twelve RAG models across seven distinct tasks, we show that incorporating fairness-aware retrieval often maintains or even enhances both ranking quality and generation quality, countering the common belief that fairness compromises system performance. Additionally, we demonstrate that fair retrieval practices lead to more balanced attribution in the final responses, ensuring that the generator fairly cites the sources it relies on. Our findings underscore the importance of item-side fairness in retrieval and generation, laying the foundation for responsible and equitable RAG systems and guiding future research in fair ranking and attribution.
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。
