Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysis

2025-12-10

AI Summary

The underlying mechanisms by which Chain-of-Thought (CoT) prompting enhances large language models’ (LLMs’) code generation performance remain poorly understood. Method: This study systematically investigates CoT’s efficacy through an information-theoretic lens—specifically, conditional mutual information (I(Y;C|X))—across a multi-scale model spectrum (7B–480B), six Python and twelve multilingual code-generation benchmarks, and complexity-stratified evaluation. Contribution/Results: We quantitatively establish that CoT effectiveness critically depends on programming language, model scale, and reasoning quality—not merely template structure. Structured CoT yields average Pass@1 improvements of 5–12% over zero-shot CoT, outperforming unstructured variants. Crucially, reasoning fidelity proves more decisive than syntactic formatting; low-quality CoT degrades performance. These findings provide empirically grounded, actionable guidelines for selecting optimal CoT strategies across model sizes and programming languages.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) achieve strong performance on code generation, but the mechanisms by which Chain-of-Thought (CoT) prompting helps remain unclear. We present a systematic empirical and information-theoretic study of CoT effectiveness in neural code generation, evaluating five paradigms (Zero-Shot, Zero-Shot CoT, Self-Planning, Structured CoT, Reasoning-CoT) across six Python benchmarks, a multilingual benchmark with 12 programming languages, and six models from 7B to 480B parameters, using conditional mutual information $I(Y;C|X)$ as a conceptual lens. Our results show that externally guided CoT consistently outperforms direct generation, with structured methods improving Pass@1 by 5--12% on average while using substantially fewer tokens than reflective reasoning, and that CoT benefits depend on language type systems and model capacity. We further find that reasoning emph{quality} is critical: high-quality structured CoT from strong generators yields significantly higher accuracy than lightweight alternatives with the same template, whereas naive Zero-Shot CoT can even degrade performance. These findings provide practical guidance for choosing CoT strategies based on model capacity, language characteristics, and task complexity.

Problem

• Analyzes how Chain-of-Thought prompting improves code generation in LLMs.
• Evaluates CoT effectiveness across models, languages, and benchmarks empirically.
• Investigates the role of reasoning quality and structured guidance in CoT.

Innovation

• Systematically evaluate five CoT paradigms across multiple benchmarks
• Use conditional mutual information as conceptual lens for analysis
• Show structured CoT improves accuracy with fewer tokens than reflective reasoning