Summary of From Introspection to Best Practices: Principled Analysis Of Demonstrations in Multimodal In-context Learning, by Nan Xu et al.
From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning
by Nan Xu, Fei Wang, Sheng Zhang, Hoifung Poon, Muhao Chen
First submitted to arxiv on: 1 Jul 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 In this paper, researchers investigate the principles behind Multimodal In-Context Learning (ICL) in Large Language Models (LLMs). They explore how models with visual modalities learn from image-text pairs and develop strategies to boost performance. The study shows that modality information matters differently across tasks and recommends modality-driven demonstration approaches. Additionally, it highlights the potential for models to follow biases learned through multimodal ICL, even if they contradict semantic priors from pre-training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal In-Context Learning (ICL) is a way that Large Language Models (LLMs) can learn from examples. Researchers are trying to understand how this works and how it can be improved. They looked at how models with visual features, like images, learn from pairs of images and text. The results show that what the model sees matters differently depending on the task. To make the most of ICL, they suggest using demonstrations that focus on specific parts of the image or text. This study helps us understand how to use examples effectively in multimodal ICL. |
