The paper investigates topic-level fairness in retrieval‑augmented generation (RAG) systems, noting that aggregate metrics can mask disparities across individual topics. It proposes adapting a reranker per topic using a Bradley‑Terry objective guided by preference pairs derived from the generator’s own output, which reduces mean absolute per‑topic disparity by 51% on RoMQA and 40% on TREC Fair Ranking while preserving accuracy. Existing baselines, even when modified to treat topics separately, fail to achieve comparable reductions.
arXiv:2610.07128v1 Announce Type: cross Abstract: Aggregating the preferences of diverse user groups into a collective outcome raises fundamental challenges of equity, diversity, and inclusion (EDI):...
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
Fair-GPTQ is a quantization method for large language models that incorporates explicit group‑fairness constraints into the quantization objective. By guiding the rounding operation, it reduces bias in generated text, particularly for occupational stereotypes and discriminatory language across gender, race, and religion. The approach maintains at least 90 % of baseline accuracy on zero‑shot benchmarks while preserving the memory and speed benefits of 4‑bit quantization.
arXiv:2606. 28343v1 Announce Type: cross Abstract: Retrieval-augmented generative agents rely on retrieval for grounding, yet are typically evaluated on a query-by-query basis.
arXiv:2608.22859v1 Announce Type: cross Abstract: RAG systems are increasingly used to summarize what large collections of documents say. A user asks "What do people think about X?" and receives an a...
arXiv:2601. 23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical resort.
arXiv:2604.20677v3 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when...
arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
Large Language Model judges are commonly used to rank texts via pairwise comparison, with reliability traditionally measured by position bias, transitivity, and pairwise agreement. This paper argues that these proxies are misleading because they are dominated by close‑rank‑gap pairs, which contribute little to the overall ranking, while far‑gap pairs carry the true ranking signal. Experiments on simulations and human‑rated corpora show weak correlation between the proxies and actual ranking accuracy, suggesting judges should be evaluated using rank‑gap‑conditional metrics against human rankings.
arXiv:2606. 00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because "funny" is audience-dependent and supervision is noisy -- preferences vary with audience, context, and culture, and annotator agreement is often low.
FairRARI is a new in‑processing convex optimization framework that computes PageRank vectors while satisfying various group‑fairness constraints based on vertex attributes. By formulating PageRank as a variational problem, FairRARI solves a strongly convex problem that guarantees a target fairness level and retains the same asymptotic time complexity as standard PageRank. Experiments on real datasets show that FairRARI outperforms existing methods in utility while achieving the desired fairness across multiple vertex groups.