  Large language models (LLMs) have emerged as versatile tools in various daily
applications. However, they are fraught with issues that undermine their
utility and trustworthiness. These include the incorporation of erroneous
references (citation), the generation of hallucinated information
(correctness), and the inclusion of superfluous or omission of crucial details
(fluency). To ameliorate these concerns, this study makes several key
contributions. First, we build a dataset to train a critic model capable of
evaluating the citation, correctness, and fluency of responses generated by
LLMs in QA systems. Second, we propose an automated feedback mechanism that
leverages the critic model to offer real-time feedback on heterogeneous aspects
of generated text. Third, we introduce a feedback learning loop that uses this
critic model to iteratively improve the performance of the LLM responsible for
response generation. Experimental results demonstrate the efficacy of our
approach, showing substantial improvements in citation and fluency metrics for
ChatGPT, including a 4% precision increase in citation and an approximately 8%
enhancement in the MAUVE metric for fluency, while maintaining high levels of
correctness.
