Summary of Mateval: a Multi-agent Discussion Framework For Advancing Open-ended Text Evaluation, by Yu Li et al.
MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation
by Yu Li, Shenyu Zhang, Rui Wu, Xiutian Huang, Yongrui Chen, Wenhao Xu, Guilin Qi, Dehai Min
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MATEval framework utilizes multiple Large Language Models (LLMs) like GPT-4 as evaluators to address the challenges in evaluating open-ended text generated by LLMs. The framework integrates self-reflection, Chain-of-Thought (CoT), and feedback mechanisms to enhance the evaluation process and guide discussions towards consensus. Experimental results show that MATEval outperforms existing methods, achieving high correlation with human evaluation and confirming its effectiveness in addressing uncertainties and instabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to evaluate text generated by large language models. Instead of using just one model as an evaluator, they use multiple models like GPT-4 to work together and discuss the quality of the text. This helps to overcome some of the limitations of previous methods and makes it more accurate and efficient. |
Keywords
» Artificial intelligence » Gpt
