Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2605.15790v1
• Date: Fri, 15 May 2026 09:47:44 GMT
• Title: Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation
• Abstract summary: We propose a fairness-aware retrieval framework that models and controls this bias.<n>Our approach combines controlled bias injection via reranking, a position-aware model of bias propagation, and an optimization formulation that balances relevance and fairness.
• Score: 5.125143539960351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization [20.259122922188126]
  Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements.<n>textbfDGAO aims to improve model accuracy and order stability simultaneously.
  arXiv  Detail & Related papers  (2026-05-12T11:31:18Z)
• Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control [82.30868101940068]
  We propose a paradigm in which a model bootstraps its own performance without reliance on external data or teacher models.<n>Our theoretical analysis shows that RSIR acts as a data-driven implicit regularizer, smoothing the optimization landscape.<n>We show that even smaller models benefit, and weak models can generate effective training curricula for stronger ones.
  arXiv  Detail & Related papers  (2026-02-17T15:31:32Z)
• Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval [0.0]
  Large Language Models (LLMs) have transformed the field of artificial intelligence by unlocking the era of generative applications.<n>Built on top of generative AI capabilities, Agentic AI represents a major shift toward autonomous, goal-driven systems that can reason, retrieve, and act.
  arXiv  Detail & Related papers  (2025-08-26T06:44:04Z)
• A Novel Generative Model with Causality Constraint for Mitigating Biases in Recommender Systems [20.672668625179526]
  Latent confounding bias can obscure the true causal relationship between user feedback and item exposure.<n>We propose a novel generative framework called Latent Causality Constraints for Debiasing representation learning in Recommender Systems.
  arXiv  Detail & Related papers  (2025-05-22T14:09:39Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models [83.8639566087953]
  We propose a direct retrieval-augmented optimization framework, named DRO, that enables end-to-end training of two key components.<n>DRO alternates between two phases: (i) document permutation estimation and (ii) re-weighted, progressively improving RAG components.<n>Our theoretical analysis reveals that DRO is analogous to policy-gradient methods in reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-05T23:54:53Z)
• Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model [71.45491434257106]
  Unified Generative Recommendation Framework (UniGRF) is a novel approach that integrates retrieval and ranking into a single generative model.<n>To enhance inter-stage collaboration, UniGRF introduces a ranking-driven enhancer module.<n>UniGRF significantly outperforms existing models on benchmark datasets.
  arXiv  Detail & Related papers  (2025-04-23T06:43:54Z)
• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task.<n>Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively.<n>We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing [14.432850893209817]
  This paper introduces Attention Pruning, a fairness-aware simulated annealing approach to prune attention heads in large language models (LLMs)<n>Our experiments show that Attention Pruning achieves up to $40%$ reduction in gender bias and outperforms the state-of-the-art bias mitigation strategies.
  arXiv  Detail & Related papers  (2025-03-20T03:02:32Z)
• Conditional Denoising Diffusion for Sequential Recommendation [62.127862728308045]
  Two prominent generative models, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. We present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser.
  arXiv  Detail & Related papers  (2023-04-22T15:32:59Z)
• Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics [0.6509758931804478]
  We propose a bias mitigation methodology based upon the construction of post-processed models with fairer regressor distributions. Our novel methodology performs optimization in low-dimensional spaces and avoids expensive model retraining.
  arXiv  Detail & Related papers  (2021-11-19T17:31:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.