Assessing the quality of Natural Language Generation (NLG) outputs, such as those produced by large language models (LLMs), poses significant challenges. Traditional approaches involve either resource-intensive human evaluations or automatic metrics, which often exhibit a low correlation with human judgment. In this study, we propose Review-Feedback-Reason (ReFeR), a novel evaluation framework for NLG using LLM agents. We rigorously test ReFeR using two pre-existing benchmark datasets on diverse NLG tasks. The proposed framework not only enhances the accuracy of NLG evaluation, surpassing previous benchmarks by $\sim$20\%, but also generates constructive feedback and significantly improves collective reasoning. This feedback is then leveraged for the creation of instruction-tuning datasets, which, when used to fine-tune smaller models like Mistral-7B, makes them extremely good evaluators, yielding a better correlation with human evaluations and performance nearly on par with GPT-3.5. We highlight the effectiveness of our methodology through its application on three reasoning benchmarks, where it outperforms most of the state-of-the-art methods, and also outperforms the reasoning capabilities of models like GPT-3.5 Turbo by $\sim$11.67\% and GPT-4 by $\sim$1\% on an average.

通过利用LLM代理使用Review-Feedback-Reason (ReFeR)的新型评估框架，本研究提出一种评估自然语言生成质量的方法，不仅能够提高NLG评估的准确性，超过以前的基准约20%，还能生成建设性反馈并显著改善集体推理的能力。该方法在三个推理基准测试中表现出色，超过大部分最先进的方法，并在平均水平上比GPT-3.5 Turbo高出约11.67%和GPT-4高出约1%。

Review-Feedback-Reason（ReFeR）：一种新颖的自然语言生成（NLG）评估和推理框架