Summary of Coevol: Constructing Better Responses For Instruction Finetuning Through Multi-agent Cooperation, by Renhao Li et al.
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
by Renhao Li, Minghuan Tan, Derek F. Wong, Min Yang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a new framework called CoEvol that leverages large language models (LLMs) to enhance the quality of responses generated through instruction fine-tuning (IFT). The authors argue that previous methods have not fully tapped into the potential of LLMs for improving data quality. To address this limitation, they develop an iterative framework that incorporates a debate-advise-edit-judge paradigm and a two-stage multi-agent debate strategy to ensure diversity and reliability of editing suggestions. The proposed approach outperforms competitive baselines on MT-Bench and AlpacaEval, demonstrating its effectiveness in enhancing instruction-following capabilities for LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoEvol is a new way to use big language models to make responses better. It works by using the model itself to help improve the answers it gives when following instructions. The authors think this will be more effective than previous methods that don’t fully use the model’s abilities. They tested CoEvol and found it worked better than other approaches on certain tasks. |
Keywords
» Artificial intelligence » Fine tuning
