Summary of How Does the Textual Information Affect the Retrieval Of Multimodal In-context Learning?, by Yang Luo et al.
How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning?
by Yang Luo, Zangwei Zheng, Zirui Zhu, Yang You
First submitted to arxiv on: 19 Apr 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 The paper presents an investigation into the effectiveness of textual information in selecting in-context examples for multimodal large language models (MLLMs). The authors find that current methods are biased towards visual data, overlooking valuable textual information. To address this, they introduce a novel supervised MLLM-retriever MSIER that uses a neural network to select examples enhancing multimodal in-context learning efficiency. This approach is validated through extensive testing across three distinct tasks, demonstrating its effectiveness. The paper also explores the influence of modalities on the training process and identifies factors contributing to the model’s success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers looked at how well large language models do when they use information from different sources, like text or images, to help them learn new things. They found that current methods are better at using visual data than textual data, even though both types of information can be helpful. To fix this problem, the authors created a new way for the models to choose which examples to use based on how well they will work together. This new method was tested with different tasks and showed good results. The paper also looked at what makes this new method work well and how it can be improved. |
Keywords
» Artificial intelligence » Neural network » Supervised
