Summary of Deeper Insights Without Updates: the Power Of In-context Learning Over Fine-tuning, by Qingyu Yin et al.
Deeper Insights Without Updates: The Power of In-Context Learning Over Fine-Tuning
by Qingyu Yin, Xuzheng He, Luoao Deng, Chak Tou Leong, Fan Wang, Yanzhao Yan, Xiaoyu Shen, Qiang Zhang
First submitted to arxiv on: 7 Oct 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 The paper presents a surprising finding that challenges the common assumption that fine-tuning can outperform in-context learning (ICL) in imbuing large language models with task-specific knowledge. The study finds that for tasks featuring implicit patterns, ICL significantly surpasses fine-tuning in capturing these patterns. To test this hypothesis, the authors developed several datasets showcasing implicit patterns and evaluated various models ranging from 0.5B to 7B parameters under both fine-tuning and ICL. The results show that ICL enables models to quickly grasp deep patterns, leading to significant accuracy improvements. In contrast, fine-tuning, despite using thousands of times more training data than ICL, achieves only limited gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at learning new tasks, but how they do it is still a mystery. The researchers found that when the task requires finding patterns in the data, one method called in-context learning (ICL) works much better than another method called fine-tuning. ICL helps the model learn by giving it examples of the task and letting it figure out what to do with them. Fine-tuning involves adjusting the model’s internal settings based on lots of training data. The study tested both methods on several tasks that required finding patterns, like recognizing when two numbers are equal or figuring out how to simplify a math problem. The results showed that ICL was much better at finding these patterns than fine-tuning. |
Keywords
» Artificial intelligence » Fine tuning
