Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding

• URL: http://arxiv.org/abs/2411.07722v1
• Date: Tue, 12 Nov 2024 11:28:50 GMT
• Title: Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding
• Authors: Zirui Shao, Chuwei Luo, Zhaoqing Zhu, Hangdi Xing, Zhi Yu, Qi Zheng, Jiajun Bu,
• Abstract summary: As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. In this paper, we define the conflicts between cognition and perception as Cognition and Perception (C&P) knowledge conflicts. We propose a novel method called Multimodal Knowledge Consistency Fine-tuning to mitigate the C&P knowledge conflicts.
• Score: 15.828455477224516
• License:
• Abstract: Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand in recent years. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, current MLLMs often face conflicts between perception and cognition. Taking a document VQA task (cognition) as an example, an MLLM might generate answers that do not match the corresponding visual content identified by its OCR (perception). This conflict suggests that the MLLM might struggle to establish an intrinsic connection between the information it "sees" and what it "understands." Such conflicts challenge the intuitive notion that cognition is consistent with perception, hindering the performance and explainability of MLLMs. In this paper, we define the conflicts between cognition and perception as Cognition and Perception (C&P) knowledge conflicts, a form of multimodal knowledge conflicts, and systematically assess them with a focus on document understanding. Our analysis reveals that even GPT-4o, a leading MLLM, achieves only 68.6% C&P consistency. To mitigate the C&P knowledge conflicts, we propose a novel method called Multimodal Knowledge Consistency Fine-tuning. This method first ensures task-specific consistency and then connects the cognitive and perceptual knowledge. Our method significantly reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks in most scenarios.

Related papers

• 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)
• Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration [107.31481207855835]
  Current methods, including intrinsic knowledge editing and external knowledge resorting, each possess strengths and weaknesses. We propose UniKE, a novel multimodal editing method that establishes a unified perspective for intrinsic knowledge editing and external knowledge resorting.
  arXiv  Detail & Related papers  (2024-09-30T02:13:53Z)
• ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM [36.332500824079844]
  Large language models (LLMs) have achieved impressive advancements across numerous disciplines, yet the critical issue of knowledge conflicts has rarely been studied. We present ConflictBank, the first comprehensive benchmark developed to evaluate knowledge conflicts from three aspects. Our investigation delves into four model families and twelve LLM instances, meticulously analyzing conflicts stemming from misinformation, temporal discrepancies, and semantic divergences.
  arXiv  Detail & Related papers  (2024-08-22T02:33:13Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• Knowledge Conflicts for LLMs: A Survey [24.731074825915833]
  Survey focuses on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of large language models.
  arXiv  Detail & Related papers  (2024-03-13T08:02:23Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration [39.603649838876294]
  We study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. Motivated by their failures in self-reflection and over-reliance on held-out sets, we propose two novel approaches.
  arXiv  Detail & Related papers  (2024-02-01T06:11:49Z)
• 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)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

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.