mlsanityData to 7 Oct 2026

Automatic Instruction Evolving for Large Language Models

arXiv 2406.00770 ↗First posted 2 Jun 2024cs.CL cs.AIcited by 24

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Abstract (arXiv)

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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 2 Jun 2024.

Builds on

21 of 28 references.

References · 21

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  8. 2023Qwen Technical Reportcited by 3,273
  9. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  10. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  11. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
  12. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
9 more
  1. 2023LIMA: Less Is More for Alignmentcited by 1,080
  2. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  3. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  4. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 712
  5. 2023What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuningcited by 354
  6. 2021ZeRO-Offload: Democratizing Billion-Scale Model Trainingcited by 339
  7. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 189
  8. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 115
  9. 2023TinyGSM: achieving >80% on GSM8k with small language modelscited by 68

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 10; 2026: 5.

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  1. 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 451
  2. 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 50
  3. 2025The Best Instruction-Tuning Data are Those That Fitcited by 48
  4. 2026Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligencecited by 35
  5. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Modelscited by 31
  6. 2025CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning taskscited by 30
  7. 2024Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generationcited by 27
  8. 2024Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arenacited by 26
  9. 2024Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratchcited by 24
  10. 2025Auto-scaling Continuous Memory for GUI Agentcited by 19
  11. 2024Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leapscited by 12
  12. 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Modelscited by 11
12 more
  1. 2025Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learningcited by 7
  2. 2025TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioningcited by 6
  3. 2025DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoningcited by 6
  4. 2025DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoningcited by 6
  5. 2026SETA: Scaling Environments for Terminal Agentscited by 5
  6. 2025Scaling Instruction-Tuned LLMs to Million-Token Contexts via Hierarchical Synthetic Data Generationcited by 4
  7. 2026FrontierSmith: Synthesizing Open-Ended Coding Problems at Scalecited by 3
  8. 2024CareBot: A Pioneering Full-Process Open-Source Medical Language Modelcited by 2
  9. 2024Learning from "Silly" Questions Improves Large Language Models, But Only Slightlycited by 0
  10. 2025Motif 2.6B Technical Reportcited by 0
  11. 2026Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusioncited by 0
  12. 2026DataFoundry: Evolving Data Preparators via Recursive Self-Improvementcited by 0