ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
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.
65430b5b2cc14ec6fda1d83a
Zejun Wang
admin_assigned
2023-11-02T13:52:15.709Z
61cbc40741f9432649f03b0b
/avatars/9e610a530f790cde539e347479f26a3e.svg
WangZeJun
user
WangZeJun
65430b5b2cc14ec6fda1d83b
Jia Li
65430b5b2cc14ec6fda1d83c
Ge Li
claimed_verified
2024-10-10T08:03:47.531Z
6530b96ef68c6e013d08b413
/avatars/ecc2874e612d0aed357d072427faf21a.svg
Ge Li
user
ligechina
65430b5b2cc14ec6fda1d83d
Zhi Jin
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
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.
ChatCoder enhances large language models' code generation by refining human requirements through interactive chat sessions, leading to better performance and precision.
large language models code generation human requirements chat scheme requirement refinement interactive chat LLMs fine-tuned