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