Summary of Is Cognition Consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding, by Zirui Shao et al.
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding
by Zirui Shao, Chuwei Luo, Zhaoqing Zhu, Hangdi Xing, Zhi Yu, Qi Zheng, Jiajun Bu
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the limitations of multimodal large language models (MLLMs) in document understanding, a growing research area with industrial demand. Current MLLMs face conflicts between perception and cognition, which hinder their performance and explainability. The authors define this conflict as Cognition and Perception (C&P) knowledge conflicts and analyze them using GPT-4o, a leading MLLM. They find that even GPT-4o achieves only 68.6% C&P consistency, highlighting the need for improvements. To mitigate these conflicts, the authors propose a novel method called Multimodal Knowledge Consistency Fine-tuning, which reduces C&P knowledge conflicts and enhances MLLMs’ performance in cognitive and perceptual tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models do when understanding documents. These models are very good at reading and processing text, but they struggle to connect what they “see” with what they “understand”. This makes it hard for them to do things like answer questions or summarize documents accurately. The authors of this paper identify the problem as a “knowledge conflict” between what the model sees (perception) and what it understands (cognition). They find that even one of the best language models, GPT-4o, only gets 68% of its answers right. To fix this problem, they suggest a new way to train the models so that they can better connect their perception and cognition. This could help them do things like summarize documents more accurately or answer questions more correctly. |
Keywords
» Artificial intelligence » Fine tuning » Gpt
