June 4, 2024, 4:53 a.m. | Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.00770v1 Announce Type: new
Abstract: Fine-tuning large pre-trained language models with Evol-Instruct has achieved encouraging results across a wide range of tasks. However, designing effective evolving methods for instruction evolution requires substantial human expertise. This paper proposes Auto Evol-Instruct, an end-to-end framework that evolves instruction datasets using large language models without any human effort. The framework automatically analyzes and summarizes suitable evolutionary strategies for the given instruction data and iteratively improves the evolving method based on issues exposed during the …

abstract arxiv auto cs.ai cs.cl datasets designing evolution expertise fine-tuning framework however human language language models large language large language models paper results tasks type

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