Summary of Multimodal Task Vectors Enable Many-shot Multimodal In-context Learning, by Brandon Huang et al.
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
by Brandon Huang, Chancharik Mitra, Assaf Arbelle, Leonid Karlinsky, Trevor Darrell, Roei Herzig
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper proposes a novel method to enable Large Multimodal Models (LMMs) for multimodal, many-shot in-context learning. This is achieved by leveraging Multimodal Task Vectors (MTV), which are compact implicit representations of in-context examples compressed in the model’s attention heads. The authors demonstrate the existence of MTV in LMMs and show that they can be used to enable many-shot in-context learning for various vision-and-language tasks. The results suggest that MTV can scale in performance with the number of compressed shots and generalize to similar out-of-domain tasks without additional context length for inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps Large Multimodal Models learn new tasks by compressing many examples into fewer tokens without needing to retrain. It finds a way to use the model’s attention heads to store important information about what it has learned, so it can use this knowledge to help with new tasks. This method works well for vision-and-language tasks and can even handle new situations without needing more training. |
Keywords
* Artificial intelligence * Attention * Context length * Inference
