Summary of Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models, by Zhijie Tan et al.
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
by Zhijie Tan, Xu Chu, Weiping Li, Tong Mo
First submitted to arxiv on: 22 Oct 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 behavior of Multimodal Large Language Models (MLLMs) when presented with multimodal contexts in different orders. Researchers found that changing the order of text, images, or videos can significantly impact model performance, sometimes leading to advanced results and other times random guessing. This phenomenon is observed in both single-modality and mixed-modality contexts. The study also reveals that popular MLLMs tend to focus on specific positions within the context, such as the beginning and end. By leveraging this attention, the authors propose a new method for placing key content in strategic positions to improve model performance by 14.7% for video-caption matching and 17.8% for visual question answering tasks. Additionally, the paper introduces Position-Invariant Accuracy (PIA) as a metric to address order bias in MLLM evaluation. These findings contribute to a better understanding of Multi-Modal In-Context Learning (MMICL) and provide practical strategies for enhancing MLLM performance without increasing computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how changing the order of text, images, or videos affects how well Multimodal Large Language Models work. The researchers found that when they changed the order, some models did really well and others didn’t do so great. This happened whether it was just text, just images, or a mix of both. They also discovered that popular models tend to pay more attention to certain parts of what’s being shown. By using this knowledge, they developed a new way to show the important parts first, which made the models work 14.7% better at matching videos with captions and 17.8% better at answering questions based on images. |
Keywords
» Artificial intelligence » Attention » Multi modal » Question answering
