| Challenge: | a promising modular AI system enhances factuality and privacy in large language models . however, each component introduces its own biases into the RAG system, which could cause representational harm and unsafe user interactions. |
| Approach: | They study the conflict between biases of each component and their relationship to the overall bias of the retrieval augmented generation system. |
| Outcome: | The proposed model can be controlled by the embedder while maintaining utility . the proposed model is more fair than existing models, the authors show . |
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| Challenge: | Retrieval-Augmented Generation (RAG) models address fairness concerns with respect to sensitive attributes such as gender, geographic location, and other demographic factors. |
| Approach: | They propose a framework to evaluate fairness in RAG using scenario-based questions and analyzing disparities across demographic attributes. |
| Outcome: | The proposed framework analyzes disparities across demographic attributes and identifies fairness issues in retrieval and generation stages. |
LLMs Trust Humans More, That’s a Problem! Unveiling and Mitigating the Authority Bias in Retrieval-Augmented Generation (2025.acl-long)
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Yuxuan Li, Xinwei Guo, Jiashi Gao, Guanhua Chen, Xiangyu Zhao, Jiaxin Zhang, Quanying Liu, Haiyan Wu, Xin Yao, Xuetao Wei
| Challenge: | Large language models (LLMs) generate outputs that stray from user input or contravene established knowledge. |
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Evaluating the Effect of Retrieval Augmentation on Social Biases (2026.eacl-long)
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| Challenge: | RAG is a popular method for injecting up-to-date knowledge into LLMs. |
| Approach: | They examine how RAG modulates social biases across three languages and four categories . they find that biased documents are amplified even when base LLM has low-level of intrinsic bias . |
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No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations. |
| Approach: | They propose a practical three-level threat model from the perspective of user fairness awareness. |
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Do RAG Systems Really Suffer From Positional Bias? (2025.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
| Approach: | They propose to use a retrieval algorithm to add passages from an external corpus to the LLM prompt to improve the factual accuracy of LLMs. |
| Outcome: | The proposed approach improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing RAG models are sensitive to the order in which evidence is presented, resulting in unstable performance and biased reasoning. |
| Approach: | They propose to quantify position bias in multimodal RAG systems by using position sensitivity index . they also develop a visualization framework to trace attention allocation patterns across decoder layers . |
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Fair RAG: End-to-End Fairness Across Retrieval and Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) can amplify demographic bias by generating skewed context . prior work treats fairness in retrieval or generation in isolation, leaving end-to-end fairness underexplored . |
| Approach: | They propose a pipeline that jointly controls both retrieval and generation stages . large language models can handle a broad set of inference tasks, they argue . |
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Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG (2026.findings-acl)
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| Challenge: | Query enhancement techniques are now standard in retrieval-augmented generation systems, yet their impact on these biases remains unexplored. |
| Approach: | They evaluate query enhancement techniques that improve retrieval quality . they find that simple rewriting reduces bias through increased score variance . no technique uniformly addresses all biases, and effects vary substantially across retrievers . |
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Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks (2025.emnlp-main)
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Gaurav Bagwe, Saket Sanjeev Chaturvedi, Xiaolong Ma, Xiaoyong Yuan, Kuang-Ching Wang, Lan Emily Zhang
| Challenge: | Retrieval-augmented generation (RAG) enhances factual grounding but introduces new attack surfaces, particularly through backdoor attacks. |
| Approach: | They propose a framework that exposes fairness vulnerabilities in RAG through a two-phase backdoor attack. |
| Outcome: | Empirical results show that BiasRAG achieves high attack success rates while remaining undetectable under standard fairness evaluations. |
Can We Instruct LLMs to Compensate for Position Bias? (2024.findings-emnlp)
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| Challenge: | Recent studies reveal that position bias in large language models (LLMs) leads to difficulty in accessing information retrieved from the retriever. |
| Approach: | They propose to direct LLMs to allocate more attention towards a selected segment of the context through prompting. |
| Outcome: | The proposed approach improves the performance of large language models by promoting instruction with an exact document index. |