Summary of Improving Multimodal Large Language Models Using Continual Learning, by Shikhar Srivastava et al.
Improving Multimodal Large Language Models Using Continual Learning
by Shikhar Srivastava, Md Yousuf Harun, Robik Shrestha, Christopher Kanan
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 This paper explores the integration of pre-trained vision models with generative large language models (LLMs) to create multimodal LLMs. The authors investigate how this integration affects natural language understanding and generation tasks, using the LLaVA MLLM as a case study. They treat the integration as a continual learning problem and evaluate five different methods to mitigate forgetting. Their approach reduces linguistic performance degradation by up to 15% while maintaining high multimodal accuracy. The paper also demonstrates the robustness of their method through continual learning on a sequence of vision-language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how combining language and vision models affects how well they can understand and generate natural language. They take an existing language model, add a pre-trained vision model to make it multimodal, and then try to keep the original language skills while also improving its ability to work with visual data. The authors test different ways to do this and find one that works well, reducing the loss of language abilities by up to 15%. They also show that their approach can learn new things from a series of vision-language tasks without losing its initial language skills. |
Keywords
* Artificial intelligence * Continual learning * Language model * Language understanding
