MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation

• URL: http://arxiv.org/abs/2403.19305v2
• Date: Mon, 15 Apr 2024 14:21:52 GMT
• Title: MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation
• Authors: Yu Li, Shenyu Zhang, Rui Wu, Xiutian Huang, Yongrui Chen, Wenhao Xu, Guilin Qi, Dehai Min,
• Abstract summary: generative Large Language Models (LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. We propose the MATEval: A "Multi-Agent Text Evaluation framework" Our framework incorporates self-reflection and Chain-of-Thought strategies, along with feedback mechanisms, to enhance the depth and breadth of the evaluation process.
• Score: 22.19073789961769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in generative Large Language Models(LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. Evaluating the quality of text generated by these models, especially in open-ended text, has consistently presented a significant challenge. Addressing this, recent work has explored the possibility of using LLMs as evaluators. While using a single LLM as an evaluation agent shows potential, it is filled with significant uncertainty and instability. To address these issues, we propose the MATEval: A "Multi-Agent Text Evaluation framework" where all agents are played by LLMs like GPT-4. The MATEval framework emulates human collaborative discussion methods, integrating multiple agents' interactions to evaluate open-ended text. Our framework incorporates self-reflection and Chain-of-Thought (CoT) strategies, along with feedback mechanisms, enhancing the depth and breadth of the evaluation process and guiding discussions towards consensus, while the framework generates comprehensive evaluation reports, including error localization, error types and scoring. Experimental results show that our framework outperforms existing open-ended text evaluation methods and achieves the highest correlation with human evaluation, which confirms the effectiveness and advancement of our framework in addressing the uncertainties and instabilities in evaluating LLMs-generated text. Furthermore, our framework significantly improves the efficiency of text evaluation and model iteration in industrial scenarios.

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