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No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users

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arxiv 2410.07589 v1 pith:DV6WN7FG submitted 2024-10-10 cs.IR cs.CL

classification cs.IRcs.CL
keywords fairnessllmscensoredgenerationalignmentawarenesscost-efficiencydatasets
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Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness and cost-efficiency truly a free lunch? In this study, we comprehensively investigate the fairness costs associated with RAG by proposing a practical three-level threat model from the perspective of user awareness of fairness. Specifically, varying levels of user fairness awareness result in different degrees of fairness censorship on the external dataset. We examine the fairness implications of RAG using uncensored, partially censored, and fully censored datasets. Our experiments demonstrate that fairness alignment can be easily undermined through RAG without the need for fine-tuning or retraining. Even with fully censored and supposedly unbiased external datasets, RAG can lead to biased outputs. Our findings underscore the limitations of current alignment methods in the context of RAG-based LLMs and highlight the urgent need for new strategies to ensure fairness. We propose potential mitigations and call for further research to develop robust fairness safeguards in RAG-based LLMs.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs

    cs.LG 2025-06 reject novelty 6.0 of 10

    A retrieval-augmented generation system can be poisoned with reward-optimized biased documents and vector-space manipulation to substantially increase biased LLM outputs.

  2. Safety Degradation in AI Agents

    cs.CY 2025-05 conditional novelty 6.0 of 10

    Adding retrieval to aligned LLMs degrades safety: refusal rates fall, bias and harmfulness rise, and prompt-based mitigation only partially restores alignment.

  3. Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval

    cs.AI 2025-08 reject novelty 4.0 of 10

    A multi-agent retrieval system that filters sources by a bias classifier reports an 81.82% relative drop in bias rate, but the evaluation uses the same classifier as the filter.

  4. Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey

    cs.CL 2025-02 conditional novelty 4.0 of 10

    A survey organizes current research on trustworthy RAG into six pillars, reliability, privacy, safety, fairness, explainability, and accountability, and maps methods, metrics, and open problems for each.

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