ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Authors: name: Zejun Wang, name: Jia Li, name: Ge Li, name: Zhi Jin

Large language models have shown good performances in generating code to meet
human requirements. However, human requirements expressed in natural languages
can be vague, incomplete, and ambiguous, leading large language models to
misunderstand human requirements and make mistakes. Worse, it is difficult for
a human user to refine the requirement. To help human users refine their
requirements and improve large language models' code generation performances,
we propose ChatCoder: a method to refine the requirements via chatting with
large language models. We design a chat scheme in which the large language
models will guide the human users to refine their expression of requirements to
be more precise, unambiguous, and complete than before. Experiments show that
ChatCoder has improved existing large language models' performance by a large
margin. Besides, ChatCoder has the advantage over refine-based methods and LLMs
fine-tuned via human response.