CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
This addresses the challenge of enhancing instruction-following capabilities in large language models for AI applications, representing an incremental improvement over existing data construction methods.
The paper tackles the problem of improving instruction fine-tuning data quality by proposing CoEvol, a multi-agent cooperation framework that refines responses through iterative debate and editing, resulting in models that outperform baselines on MT-Bench and AlpacaEval benchmarks.
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.