Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation

Understand

Large language models (LLMs) have showcased remarkable prowess in code generation.

• However, automated code generation is still challenging since it requires a high-level semantic mapping between natural language requirements and codes.
• Most existing LLMs-based approaches for code generation rely on decoder-only causal language models often treate codes merely as plain text tokens, i.e., feeding the requirements as a prompt input, and outputing code as flat sequence of tokens, potentially missing the rich semantic features inherent in source code.
• To bridge this gap, this paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT.