  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.
