Summary of What Makes Multimodal In-context Learning Work?, by Folco Bertini Baldassini et al.
What Makes Multimodal In-Context Learning Work?
by Folco Bertini Baldassini, Mustafa Shukor, Matthieu Cord, Laure Soulier, Benjamin Piwowarski
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A comprehensive framework for investigating Multimodal In-Context Learning (M-ICL) in large multimodal models is presented. The study considers open-source multimodal models and various tasks, revealing that M-ICL relies heavily on text-driven mechanisms with little influence from image modality. While an advanced ICL strategy (RICES) doesn’t improve performance over a simple majority voting strategy, the study identifies biases and limitations of M-ICL that need consideration before deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can learn new skills quickly, even with just a few examples. This paper looks at how these models work when they’re given images as well as text to learn from. The results show that the models are mostly influenced by the text, and don’t use the images as much. This could be important for things like training AI systems that can understand both written and spoken language. |
