Summary of Many-shot In-context Learning in Multimodal Foundation Models, by Yixing Jiang et al.
Many-Shot In-Context Learning in Multimodal Foundation Models
by Yixing Jiang, Jeremy Irvin, Ji Hun Wang, Muhammad Ahmed Chaudhry, Jonathan H. Chen, Andrew Y. Ng
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates the capabilities of large language models in performing few-shot and many-shot in-context learning (ICL) with multimodal foundation models. The researchers evaluate GPT-4o, Gemini 1.5 Pro, and open-weights models like Llama 3.2-Vision across 14 datasets from various domains and tasks. They find that many-shot ICL leads to substantial improvements compared to few-shot ICL, with Gemini 1.5 Pro showing log-linear performance improvements up to a maximum number of tested examples. The study also explores the impact of batching multiple queries in a single API call, demonstrating performance gains under zero-shot and many-shot ICL. Results suggest that many-shot ICL could enable users to efficiently adapt multimodal foundation models to new applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can learn from small examples. They tested different models on 14 sets of data with different types of information (like images, medical reports, and more). The results show that the more examples they use, the better the models get at doing tasks like recognizing objects or answering questions. One model, called Gemini 1.5 Pro, gets even better as it uses more examples. They also tested how well these models work when many people ask similar questions at once, and found that this can actually make them do their job faster. |
Keywords
» Artificial intelligence » Few shot » Gemini » Gpt » Llama » Zero shot
