Many language models now enhance their responses with retrieval capabilities, leading to the widespread adoption of retrieval-augmented generation (RAG) systems. However, despite retrieval being a core component of RAG, much of the research in this area overlooks the extensive body of work on fair ranking, neglecting the importance of considering all stakeholders involved. This paper presents the first systematic evaluation of RAG systems integrated with fair rankings. We focus specifically on measuring the fair exposure of each relevant item across the rankings utilized by RAG systems (i.e., item-side fairness), aiming to promote equitable growth for relevant item providers. To gain a deep understanding of the relationship between item-fairness, ranking quality, and generation quality in the context of RAG, we analyze nine different RAG systems that incorporate fair rankings across seven distinct datasets. Our findings indicate that RAG systems with fair rankings can maintain a high level of generation quality and, in many cases, even outperform traditional RAG systems, despite the general trend of a tradeoff between ensuring fairness and maintaining system-effectiveness. We believe our insights lay the groundwork for responsible and equitable RAG systems and open new avenues for future research. We publicly release our codebase and dataset at https://github.com/kimdanny/Fair-RAG.

本研究解决了检索增强生成(RAG)系统中公平排序被忽视的问题。我们首次系统评估了集成公平排序的RAG系统，发现它们在生成质量方面能够与传统RAG系统相媲美，甚至在许多情况下超越后者，从而推动了相关项目提供者的公平增长。我们的研究为负责任与公平的RAG系统奠定了基础，并为未来研究开辟了新方向。

朝着公平的检索增强生成：公平排序对检索增强生成的影响