Assisting Research Proposal Writing with Large Language Models: Evaluation and Refinement

In this study, we employ ChatGPT -4o to generate academically sound, high-quality research proposals. T o evaluate the writing capabilities and potential of LLMs, we adopt both standard GPT -only and GPT -assisted writing approaches. T o effectively assess the writing capabilities of LLMs, we introduce two key evaluation metrics: content quality and reference validity . Additionally, we implement an iterative prompting method aimed at enhancing content quality and reducing inaccuracies and fabrications in references generated by LLMs. Our results show that the dual-metrics evaluation rigorously quantifies ChatGPT's writing capabilities, while iterative prompting enhances content quality, reduces errors, and addresses ethical concerns in reference generation. This proposal writing, evaluation, and improvement framework offers users a practical way to generate high-quality research proposals tailored to their needs. Future research can build upon this work by developing more efficient writing strategies and advanced methods to further enhance the writing capabilities of LLMs.