Summary of Multimodal Contrastive In-context Learning, by Yosuke Miyanishi et al.
Multimodal Contrastive In-Context Learning
by Yosuke Miyanishi, Minh Le Nguyen
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 novel multimodal contrastive in-context learning framework is introduced to enhance our understanding of gradient-free in-context learning (ICL) in Large Language Models (LLMs). The framework presents a contrastive learning-based interpretation of ICL in real-world settings, marking the distance of the key-value representation as the differentiator. An analytical framework is developed to address biases in multimodal input formatting for real-world datasets. The approach demonstrates effectiveness in detecting hateful memes, a task where typical ICL struggles due to resource limitations. Extensive experiments on multimodal datasets reveal that the approach significantly improves ICL performance across various scenarios, including challenging tasks and resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for Large Language Models (LLMs) to learn without needing gradients. This helps us understand how they work better in real-world situations. The researchers created a special learning method that can handle different types of input data and is good at finding hateful memes, which is important because it’s hard to detect these things quickly. They tested their idea on many datasets and showed that it works well in different situations. |
