Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2506.11063v1
• Date: Fri, 30 May 2025 06:48:02 GMT
• Title: Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation
• Authors: Jiayu Yao, Shenghua Liu, Yiwei Wang, Lingrui Mei, Baolong Bi, Yuyao Ge, Zhecheng Li, Xueqi Cheng,
• Abstract summary: We present the first comprehensive study of position bias in multimodal RAG systems.<n>Our results reveal that multimodal interactions intensify position bias compared to unimodal settings.<n>These findings highlight the need for evidence reordering or debiasing strategies to build more reliable and equitable generation systems.
• Score: 39.545788636148025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustness of these systems is critical. However, current RAG models are highly sensitive to the order in which evidence is presented, often resulting in unstable performance and biased reasoning, particularly as the number of retrieved items or modality diversity grows. This raises a central question: How does the position of retrieved evidence affect multimodal RAG performance? To answer this, we present the first comprehensive study of position bias in multimodal RAG systems. Through controlled experiments across text-only, image-only, and mixed-modality tasks, we observe a consistent U-shaped accuracy curve with respect to evidence position. To quantify this bias, we introduce the Position Sensitivity Index ($PSI_p$) and develop a visualization framework to trace attention allocation patterns across decoder layers. Our results reveal that multimodal interactions intensify position bias compared to unimodal settings, and that this bias increases logarithmically with retrieval range. These findings offer both theoretical and empirical foundations for position-aware analysis in RAG, highlighting the need for evidence reordering or debiasing strategies to build more reliable and equitable generation systems.

Related papers

• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes [14.331322509462419]
  Social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifest in biases in downstream tasks.<n>We introduce a controlled framework to measure this propagation by correlating intrinsic measures of bias in the representational space with measures of bias in zero-shot text-to-image (TTI) and image-to-text (ITT) retrieval.<n>Results show substantial correlations between intrinsic and extrinsic bias, with an average $rho$ = 0.83 $pm$ 0.10.<n> Notably, we find that larger/better-performing models exhibit greater bias propagation, a finding that raises concerns
  arXiv  Detail & Related papers  (2025-06-06T20:01:32Z)
• UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities [53.76854299076118]
  UniversalRAG is a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities.<n>We propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it.<n>We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over various modality-specific and unified baselines.
  arXiv  Detail & Related papers  (2025-04-29T13:18:58Z)
• AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning [61.28113271728859]
  RAG has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>Standard RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>In this work, we reinterpret RAG as Retrieval-Augmented Reasoning and identify a central but underexplored problem: textitReasoning Misalignment.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework.<n>It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge.<n>The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• Unmasking Conversational Bias in AI Multiagent Systems [1.0705399532413618]
  biases that may arise in multi-agent systems involving generative models remain under-researched.<n>We present a framework designed to quantify biases within multi-agent systems of conversational Large Language Models.<n>The bias observed in the echo-chamber experiment remains undetected by current state-of-the-art bias detection methods.
  arXiv  Detail & Related papers  (2025-01-24T09:10:02Z)
• Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems [18.926129063000264]
  We aim to empirically evaluate fairness in several Retrieval-Augmented Generation (RAG) methods.<n>Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages.<n>These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline.
  arXiv  Detail & Related papers  (2024-09-29T22:04:26Z)
• Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback [41.88662700261036]
  RAG systems face limitations when semantic relevance alone does not guarantee improved generation quality. We propose Pistis-RAG, a new RAG framework designed with a content-centric approach to better align LLMs with human preferences.
  arXiv  Detail & Related papers  (2024-06-21T08:52:11Z)
• DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization [58.704753031608625]
  Time series is one of the most challenging modalities in machine learning research. OOD detection and generalization on time series tend to suffer due to its non-stationary property. We propose DIVERSIFY, a framework for OOD detection and generalization on dynamic distributions of time series.
  arXiv  Detail & Related papers  (2023-08-04T12:27:11Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)

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.