Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models

• URL: http://arxiv.org/abs/2505.19509v1
• Date: Mon, 26 May 2025 04:39:30 GMT
• Title: Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models
• Authors: Yifan Jia, Kailin Jiang, Yuyang Liang, Qihan Ren, Yi Xin, Rui Yang, Fenze Feng, Mingcai Chen, Hengyang Lu, Haozhe Wang, Xiaoye Qu, Dongrui Liu, Lizhen Cui, Yuntao Du,
• Abstract summary: We propose MMKC-Bench, a benchmark to evaluate factual knowledge conflicts in both context-memory and inter-context scenarios.<n> MMKC-Bench includes 1,573 knowledge instances and 3,381 images across 23 broad types, collected through automated pipelines with human verification.<n>Our findings show that while current LMMs are capable of recognizing knowledge conflicts, they tend to favor internal parametric knowledge over external evidence.
• Score: 23.37800506729006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Multimodal Models(LMMs) face notable challenges when encountering multimodal knowledge conflicts, particularly under retrieval-augmented generation(RAG) frameworks where the contextual information from external sources may contradict the model's internal parametric knowledge, leading to unreliable outputs. However, existing benchmarks fail to reflect such realistic conflict scenarios. Most focus solely on intra-memory conflicts, while context-memory and inter-context conflicts remain largely investigated. Furthermore, commonly used factual knowledge-based evaluations are often overlooked, and existing datasets lack a thorough investigation into conflict detection capabilities. To bridge this gap, we propose MMKC-Bench, a benchmark designed to evaluate factual knowledge conflicts in both context-memory and inter-context scenarios. MMKC-Bench encompasses three types of multimodal knowledge conflicts and includes 1,573 knowledge instances and 3,381 images across 23 broad types, collected through automated pipelines with human verification. We evaluate three representative series of LMMs on both model behavior analysis and conflict detection tasks. Our findings show that while current LMMs are capable of recognizing knowledge conflicts, they tend to favor internal parametric knowledge over external evidence. We hope MMKC-Bench will foster further research in multimodal knowledge conflict and enhance the development of multimodal RAG systems. The source code is available at https://github.com/MLLMKCBENCH/MLLMKC.

Related papers

• MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation [4.177310099979434]
  Knowledge conflict often arises in RAG systems, where retrieved documents may be inconsistent with one another or contradict the model's parametric knowledge.<n>We propose a knowledge graph (KG)-based framework that generates varied and subtle conflicts between two similar yet distinct contexts.<n> Experimental results on our benchmark, MAGIC, provide intriguing insights into the inner workings of LLMs regarding knowledge conflict.
  arXiv  Detail & Related papers  (2025-07-29T07:19:49Z)
• Robust Multimodal Large Language Models Against Modality Conflict [94.12341487880465]
  multimodal large language models (MLLMs) are prone to hallucinations in real-world scenarios.<n>We study the inherent conflicts in inputs from different modalities that place MLLMs in a dilemma and directly lead to hallucinations.<n>Three methods are proposed to alleviate the hallucination caused by modality conflict.
  arXiv  Detail & Related papers  (2025-07-09T11:18:38Z)
• What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge Conflict on Large Language Models [16.41477610681199]
  Large language models frequently rely on both contextual input and parametric knowledge to perform tasks.<n>These sources can come into conflict, especially when retrieved documents contradict the model's parametric beliefs.<n>We propose a diagnostic framework to systematically evaluate LLM behavior under context-memory conflict.
  arXiv  Detail & Related papers  (2025-06-06T19:20:23Z)
• CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG [53.950029990391066]
  Cross-source knowledge textbfReconciliation for Multimodal RAG (CoRe-MMRAG)<n>We propose a novel end-to-end framework that effectively reconciles inconsistencies across knowledge sources.<n>Experiments on KB-VQA benchmarks show that CoRe-MMRAG achieves substantial improvements over baseline methods.
  arXiv  Detail & Related papers  (2025-06-03T07:32:40Z)
• Retrieval-Augmented Generation with Conflicting Evidence [57.66282463340297]
  Large language model (LLM) agents are increasingly employing retrieval-augmented generation (RAG) to improve the factuality of their responses.<n>In practice, these systems often need to handle ambiguous user queries and potentially conflicting information from multiple sources.<n>We propose RAMDocs (Retrieval with Ambiguity and Misinformation in Documents), a new dataset that simulates complex and realistic scenarios for conflicting evidence for a user query.
  arXiv  Detail & Related papers  (2025-04-17T16:46:11Z)
• SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models [6.52323086990482]
  Vision language models (VLM) demonstrate sophisticated multimodal reasoning yet are prone to hallucination when confronted with knowledge conflicts.<n>This research introduces segsub, a framework for applying targeted image perturbations to investigate VLM resilience against knowledge conflicts.
  arXiv  Detail & Related papers  (2025-02-19T00:26:38Z)
• The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio [118.75449542080746]
  This paper presents the first systematic investigation of hallucinations in large multimodal models (LMMs) Our study reveals two key contributors to hallucinations: overreliance on unimodal priors and spurious inter-modality correlations. Our findings highlight key vulnerabilities, including imbalances in modality integration and biases from training data, underscoring the need for balanced cross-modal learning.
  arXiv  Detail & Related papers  (2024-10-16T17:59:02Z)
• Insight Over Sight? Exploring the Vision-Knowledge Conflicts in Multimodal LLMs [55.74117540987519]
  This paper explores the problem of commonsense-level vision-knowledge conflict in Multimodal Large Language Models (MLLMs) We introduce an automated pipeline, augmented with human-in-the-loop quality control, to establish a benchmark aimed at simulating and assessing the conflicts in MLLMs. We evaluate the conflict-resolution capabilities of nine representative MLLMs across various model families and find a noticeable over-reliance on textual queries.
  arXiv  Detail & Related papers  (2024-10-10T17:31:17Z)
• ECon: On the Detection and Resolution of Evidence Conflicts [56.89209046429291]
  The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems. This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
  arXiv  Detail & Related papers  (2024-10-05T07:41:17Z)
• Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models [33.76903352835436]
  Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs. These models are prone to parametric knowledge conflicts, which arise from inconsistencies of represented knowledge between their vision and language components. We present a systematic approach to detect, interpret, and mitigate them.
  arXiv  Detail & Related papers  (2024-10-04T17:59:28Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning.<n>Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies.<n>We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• Resolving Knowledge Conflicts in Large Language Models [46.903549751371415]
  Large language models (LLMs) often encounter knowledge conflicts. We ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We introduce an evaluation framework for simulating contextual knowledge conflicts.
  arXiv  Detail & Related papers  (2023-10-02T06:57:45Z)

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.