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        <title>Automatic Instruction Evolving for Large Language Models</title>
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        <namePart type="given">Weihao</namePart>
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        <namePart type="given">Can</namePart>
        <namePart type="family">Xu</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Jian-Guang</namePart>
        <namePart type="family">Lou</namePart>
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    <name type="personal">
        <namePart type="given">Weizhu</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <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 instruction evolution process. Our extensive experiments demonstrate that the best method optimized by Auto Evol-Instruct outperforms human-designed methods on various benchmarks, including MT-Bench, AlpacaEval, GSM8K, and HumanEval.</abstract>
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%0 Conference Proceedings
%T Automatic Instruction Evolving for Large Language Models
%A Zeng, Weihao
%A Xu, Can
%A Zhao, Yingxiu
%A Lou, Jian-Guang
%A Chen, Weizhu
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zeng-etal-2024-automatic
%X 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 instruction evolution process. Our extensive experiments demonstrate that the best method optimized by Auto Evol-Instruct outperforms human-designed methods on various benchmarks, including MT-Bench, AlpacaEval, GSM8K, and HumanEval.
%U https://aclanthology.org/2024.emnlp-main.397
%P 6998-7018

Markdown (Informal)

[Automatic Instruction Evolving for Large Language Models](https://aclanthology.org/2024.emnlp-main.397) (Zeng et al., EMNLP 2024)

• Automatic Instruction Evolving for Large Language Models (Zeng et al., EMNLP 2024)

ACL

• Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, and Weizhu Chen. 2024. Automatic Instruction Evolving for Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6998–7018, Miami, Florida, USA. Association for Computational Linguistics.