MoT: Modularization-of-Thought Prompting for Effective Code Generation
A new prompting technique called MoT (Modularization of Thought) has been proposed to improve code generation by large language models. MoT decomposes complex programming problems into smaller, independent reasoning steps using a Multi-Level Reasoning Graph, enabling a more structured and interpretable approach. Experiments with GPT-4o-mini and DeepSeek-R1 on eight benchmarks show that MoT outperforms existing methods like Chain-of-Thought, with Pass@1 scores between 58.1% and 95.1%.
Why it matters: MoT could enhance the ability of language models to solve complex programming tasks by providing a more modular and interpretable reasoning process.
Full story at: arXiv Software Engineering ↗