Summary of Speculative Coreset Selection For Task-specific Fine-tuning, by Xiaoyu Zhang et al.
Speculative Coreset Selection for Task-Specific Fine-tuning
by Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Chao Shen, Tianlin Li, Weipeng Jiang, Yang Liu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces a novel approach to task-specific fine-tuning of large language models (LLMs), addressing limitations in existing coreset selection methods. The proposed method, called STAFF, leverages a small model from the same family as the target LLM to efficiently estimate data scores and allocate selection budget to important regions while maintaining coverage of easy regions. The authors evaluate STAFF on three LLMs and three downstream tasks, showing improved performance by up to 54.3% and reduced selection overhead by up to 70.5%. Additionally, they observe that the coreset selected at low pruning rates can even outperform the full dataset in fine-tuning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make big language models work better for specific tasks without needing lots of computer power or time. The problem is that current methods don’t always pick the most important data points, which hurts their performance. The new method, called STAFF, uses a smaller model to quickly figure out which data points are most important and then checks with the big model to make sure it’s correct. This makes the process faster and more accurate. In tests, this new approach did up to 54% better than other methods and used 70.5% less computer power. |
Keywords
» Artificial intelligence » Fine tuning » Pruning
