Summary of Supervised Fine-tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns, by Yang Zhao et al.
Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
by Yang Zhao, Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Medium Difficulty summary: This paper addresses the limitation of Large Language Models (LLMs) in performing well on complex tasks. Currently, LLMs learn through a data-driven approach, which is challenged by the scarcity and difficulty of collecting or constructing task instructions for complex tasks. However, LLMs can quickly adapt to simpler tasks with pre-trained knowledge. To enhance the efficiency and effectiveness of LLMs’ ability to learn complex tasks, this study employs a gradient-based method to dissect the process of Self-Fine-Tuning (SFT) on attention patterns. The findings reveal that LLMs selectively activate task-specific attention heads during SFT, combining basic task patterns for complex tasks, and that modifying a few parameters can significantly impact activation patterns after SFT. Building upon these insights, experiments are conducted to improve the efficiency and effectiveness of SFT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research helps Large Language Models (LLMs) do better on tricky tasks. Currently, LLMs learn from lots of data, but it’s hard to get instructions for those complex tasks. Surprisingly, LLMs can quickly pick up simpler tasks with some background knowledge. To make LLMs better at learning complex tasks, this study looks at how Self-Fine-Tuning (SFT) works on attention patterns. The results show that LLMs focus on specific parts of tasks and combine basic patterns for tricky ones. By tweaking a few settings, SFT can be improved. |
Keywords
* Artificial intelligence * Attention * Fine tuning
