面向 RAG 主题级公平性的重排序适配研究
Topic-Level Fairness in Retrieval-Augmented Generation
AI 导读
一篇 arXiv 论文提出在检索增强生成（RAG）中研究主题级公平性：跨查询聚合的公平指标可能掩盖各主题内的群体偏差。作者按主题分别用 Bradley-Terry 目标适配重排序器，偏好对由生成器自身输出构建。仅用 RoMQA 构建的偏好对，即在 RoMQA 上将平均绝对每主题偏差降低 51%、在 TREC Fair Ranking 上降低 40%，且保持准确率，优于单一重排序器等基线。
正文 · 原文
Abstract:Group fairness in retrieval-augmented generation (RAG) systems is typically evaluated using metrics aggregated across queries. However, such metrics can conceal topic-level disparities: a system may favor women on questions about human rights and men on questions about actors and filmmakers, appearing nearly neutral overall even though neither topic is fair. We therefore study topic-level fairness, which requires group fairness to hold within each topic rather than only in aggregate. To reduce the topic-level disparity, we adapt the reranker separately for each topic with a Bradley-Terry objective under direct supervision from the generated output: preference pairs built from the generator's own output carry the direction that topic requires. With preference pairs built from RoMQA alone, the adaptation reduces the mean absolute per-topic disparity by 51% on RoMQA and by 40% on TREC Fair Ranking while retaining accuracy. No baseline reaches the reduction, even when modified to treat each topic separately rather than apply one correction to every topic. Neither does our method with one reranker for all topics, which follows the direction that most topics require.
| Comments: | 21 pages, 8 figures, 16 tables |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34683 [cs.AI] |
| (or arXiv:2610.34683v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34683 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jaeyoung Park [view email]
[v1]
Mon, 26 Oct 2026 01:19:50 UTC (484 KB)
来源：arXiv cs.AI · arxiv.org