解决RAG中检索偏差传播问题，提升生成内容公平性。
Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation
- 通过重排序注入可控偏差，建模多文档联合影响
- 在保持相关性的前提下，减少生成结果偏差
- 提出FARO方法，高效实现大规模公平性优化
检索增强生成（RAG）通过引入外部知识提升大模型可靠性，但检索过程可能引入偏差并传递至生成结果。该问题在top-k检索场景尤为突出，因多个文档共同影响生成。本文提出一种公平性感知的检索框架，结合受控偏差注入的重排序、位置敏感的偏差传播模型，以及兼顾相关性与公平性的优化公式。进一步提出基于双超平面近似的二次公平性优化（FARO）方案，通过问题分解实现高效优化。实验表明，该方法能有效缓解生成偏差，同时保持内容相关性。本工作为RAG系统中的公平性检索提供了原则性解决方案。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) improves reliability of large language models by incorporating external knowledge, but the retrieval process can introduce bias that propagates to generated outputs. This issue is particularly challenging in top-k settings, where multiple documents jointly influence generation. We propose a fairness-aware retrieval framework that models and controls this bias. Our approach combines controlled bias injection via reranking, a position-aware model of bias propagation, and an optimization formulation that balances relevance and fairness. We further introduce a scalable solution based on Quadratic Fairness via Dual Hyperplane Approximation (FARO), which enables efficient optimization through problem decomposition. Experimental results show that our method effectively mitigates generation bias while preserving relevance. This work provides a principled approach for fairness-aware retrieval in RAG systems.
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