mlsanityData to 7 Oct 2026

CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation

arXiv 2406.07054 ↗First posted 11 Jun 2024cs.CL cs.AIcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

In recent years, instruction fine-tuning (IFT) on large language models (LLMs) has garnered considerable attention to enhance model performance on unseen tasks. Attempts have been made on automatic construction and effective selection for IFT data. However, we posit that previous methods have not fully harnessed the potential of LLMs for enhancing data quality. The responses within IFT data could be further enhanced by leveraging the capabilities of LLMs themselves. In this paper, we propose CoEvol, an LLM-based multi-agent cooperation framework for the improvement of responses to instructions. To effectively refine the responses, we develop an iterative framework following a debate-advise-edit-judge paradigm. A two-stage multi-agent debate strategy is further devised to ensure the diversity and reliability of editing suggestions within the framework. Empirically, models equipped with CoEvol outperform competitive baselines evaluated by MT-Bench and AlpacaEval, demonstrating its effectiveness in enhancing instruction-following capabilities for LLMs.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 29 of its 46 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

29 of 46 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate14 Aug
  2. 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration11 Jul
  3. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 26

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  7. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  8. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  9. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  10. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  11. 2023LIMA: Less Is More for Alignmentcited by 1,080
  12. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
14 more
  1. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  2. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
  3. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 738
  4. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  5. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 514
  6. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  7. 2023What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuningcited by 354
  8. 2021ZeRO-Offload: Democratizing Billion-Scale Model Trainingcited by 339
  9. 2023From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuningcited by 301
  10. 2022PromptSource: An Integrated Development Environment and Repository for Natural Language Promptscited by 288
  11. 2023#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Modelscited by 108
  12. 2020Look at the First Sentence: Position Bias in Question Answeringcited by 83
  13. 2023A Preliminary Study of the Intrinsic Relationship between Complexity and Alignmentcited by 33
  14. 2023Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Explorationcited by 18

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Applicationcited by 54
  2. 2025Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Modelscited by 8

Similar, not cited

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  1. 2024Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
  2. 2026Enhancing LLM Instruction Following: An Evaluation-Driven Multi-Agentic Workflow for Prompt Instructions Optimization
  3. 2023AgentTuning: Enabling Generalized Agent Abilities for LLMs
  4. 2025Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
  5. 2025Multi-Agent Collaboration for Multilingual Code Instruction Tuning
  6. 2023Tuna: Instruction Tuning using Feedback from Large Language Models
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  8. 2025MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching