Summary of What Factors Affect Multi-modal In-context Learning? An In-depth Exploration, by Libo Qin et al.
What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration
by Libo Qin, Qiguang Chen, Hao Fei, Zhi Chen, Min Li, Wanxiang Che
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 factors affecting the performance of Multi-Modal In-Context Learning (MM-ICL), which has achieved notable success across various tasks without requiring additional parameter tuning. The authors conduct extensive experiments on the three core steps of MM-ICL, including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. The findings highlight the importance of a multi-modal retriever for demonstration retrieval, intra-demonstration ordering over inter-demonstration ordering, and introductory instructions in prompts enhancing task comprehension. This study aims to serve as a foundational guide for optimizing MM-ICL strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about learning new things by combining different types of information. It looks at how good this method is called Multi-Modal In-Context Learning (MM-ICL) and what makes it work well or not so well. The researchers did lots of tests to see which parts are important, like finding the right examples and putting them in order, and they found some surprising things! They hope their findings will help other people make MM-ICL better. |
Keywords
» Artificial intelligence » Multi modal » Prompt
