Summary of Cross-modal Information Flow in Multimodal Large Language Models, by Zhi Zhang et al.
Cross-modal Information Flow in Multimodal Large Language Models
by Zhi Zhang, Srishti Yadav, Fengze Han, Ekaterina Shutova
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. A study aims to fill a knowledge gap by examining the information flow between linguistic and visual information within MLLMs, focusing on visual question answering. The research investigates how MLLMs combine visual and linguistic information to generate predictions, using models from the LLaVA series. Findings suggest two distinct stages of integration: transferring general visual features to linguistic representations in lower layers, specific object-based visual information to relevant token positions in middle layers, and finally propagating multimodal representation for the final prediction in higher layers. This study provides a comprehensive perspective on image and language processing in MLLMs, facilitating future research into multimodal information localization and editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can understand both words and pictures! Researchers wanted to know how these models combine visual and linguistic information. They used special computer programs called LLaVA models to see what happens when they show a picture and ask a question together. The results showed that the model does two main things: first, it takes general features from the whole picture and uses them for words in the question. Then, it looks at specific parts of the picture that are important for answering the question and combines those with the words. This helps us understand how these models work and can help us make new ones that are even better! |
Keywords
» Artificial intelligence » Question answering » Token
