Summary of Understanding Multimodal Hallucination with Parameter-free Representation Alignment, by Yueqian Wang et al.
Understanding Multimodal Hallucination with Parameter-Free Representation Alignment
by Yueqian Wang, Jianxin Liang, Yuxuan Wang, Huishuai Zhang, Dongyan Zhao
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 factors contributing to object hallucinations in Multimodal Large Language Models (MLLMs). The authors propose a parametric-free representation alignment metric (Pfram) to analyze image representations, which shows strong correlations with object hallucination across various MLLMs. They also explore other issues related to image representations, such as module roles and textual instruction impacts. By leveraging Pfram, the study aims to improve MLLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to understand why some big language models make mistakes by showing objects that aren’t really there. To figure this out, they came up with a new way to measure how similar different representation systems are. They found that this method is closely linked to the mistake rate across many state-of-the-art models. The study also looks into what makes these models better or worse at recognizing images and gives tips on how to improve them. |
Keywords
* Artificial intelligence * Alignment * Hallucination
