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Automatic Instruction Evolving for Large Language Models

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arxiv 2406.00770 v1 pith:J5UA7MRF submitted 2024-06-02 cs.CL cs.AI

Weihao Zeng , Can Xu , Yingxiu Zhao , Jian-Guang Lou , Weizhu Chen This is my paper · ORCID
classification cs.CLcs.AI
keywords instructionevol-instructevolvinglanguagelargemodelsautoevolution
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tag-Evol: Achieving Efficient Instruction Evolving via Tag Injection

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Tag-Evol generates harder, more diverse instruction data by injecting sampled knowledge tags into seed instructions, improving downstream SFT accuracy across math, code, and general benchmarks.

  2. Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A large synthetic instruction corpus with guidelines, preference rules, and format variants improves LLM performance on five NLU benchmarks by an average of 3.1%.

  3. Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing

    cs.LG 2025-02 conditional novelty 5.0 of 10

    Mediator merges fine-tuned LLMs by averaging low-conflict layers and routing high-conflict layers through sparse task-arithmetic experts, with uncertainty-based selection.

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