Summary of Automatic Instruction Evolving For Large Language Models, by Weihao Zeng et al.
Automatic Instruction Evolving for Large Language Models
by Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen
First submitted to arxiv on: 2 Jun 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 This paper proposes an end-to-end framework called Auto Evol-Instruct, which evolves instruction datasets using large pre-trained language models without requiring any human expertise. The framework automatically analyzes and summarizes suitable evolutionary strategies for the given instruction data, then iteratively improves the evolving method based on issues exposed during the instruction evolution process. The results show that the best method optimized by Auto Evol-Instruct outperforms human-designed methods on various benchmarks, including MT-Bench, AlpacaEval, GSM8K, and HumanEval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to improve language models using a special technique called Auto Evol-Instruct. It helps the model learn from its own mistakes and get better without needing humans to help. The method is tested on many tasks and shows that it can do just as well or even better than what people come up with. |
