Summary of Learn From Downstream and Be Yourself in Multimodal Large Language Model Fine-tuning, by Wenke Huang et al.
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
by Wenke Huang, Jian Liang, Zekun Shi, Didi Zhu, Guancheng Wan, He Li, Bo Du, Dacheng Tao, Mang Ye
First submitted to arxiv on: 17 Nov 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 The paper proposes a novel approach to fine-tune Multimodal Large Language Models (MLLM) for specific tasks while preserving their pre-training knowledge. By analyzing parameter importance for both pre-trained and fine-tuning distributions, the authors develop an importance-aware weight allocation strategy that selectively updates crucial modules. The strategy aims to balance generalization and specialization performance in MLLM fine-tuning. The paper conducts experiments on image captioning and visual question-answering tasks using various MLLM architectures, demonstrating the effectiveness of the proposed solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machine learning models remember what they learned before being trained for a specific task. It does this by identifying important parts of the model that need to be updated and leaving the rest unchanged. This approach improves how well the model performs on its original job while also reducing forgetting. The results show that this method works well for image captioning and visual question-answering tasks. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Image captioning » Machine learning » Question answering
