Summary of Enhancing Large Language Model Performance with Gradient-based Parameter Selection, by Haoling Li et al.
Enhancing Large Language Model Performance with Gradient-Based Parameter Selection
by Haoling Li, Xin Zhang, Xiao Liu, Yeyun Gong, Yifan Wang, Qi Chen, Peng Cheng
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 propose a novel method called Gradient-Mask Tuning (GMT) to improve the fine-tuning process of large language models (LLMs). The authors argue that existing methods fail to leverage task-specific information to identify important parameters during training. GMT selectively updates parameters based on their gradient information, computed as the absolute values of gradients with relatively smaller magnitudes masked out. Experimental results across various tasks demonstrate that GMT outperforms traditional fine-tuning methods and elevates the upper limits of LLM performance. GMT also exhibits insensitivity to mask ratio and comparable computational efficiency to vanilla SFT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have changed many areas of research. To make these models better, researchers need to update them with new information. Some people think that updating only a few parts can be enough. However, they don’t use the specific information about the task to decide what parts are most important. This paper proposes a new way called Gradient-Mask Tuning (GMT) to update LLMs. GMT looks at how much each part changes during training and updates the parts that change the least. The results show that GMT is better than traditional methods and can make LLMs perform even better. |
Keywords
» Artificial intelligence » Fine tuning » Mask
