Self-correction methods fail to improve LLM code generation without verification

A new study on arXiv investigates the effectiveness of self-correction methods for large language models (LLMs) in code generation. Researchers found that while some uncertainty estimation techniques correlate weakly with correctness, they do not reliably improve performance on benchmarks like HumanEval and BigCodeBench. Only verification-based self-correction, which involves executing code, showed consistent gains in accuracy, suggesting that uncertainty signals alone are insufficient for improving code generation quality. AI

IMPACT Highlights the limitations of uncertainty estimation for improving LLM code generation, emphasizing the need for execution-based verification.

RANK_REASON Academic paper detailing empirical study of LLM self-correction in code generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXivLabs
• BigCodeBench
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• HumanEval
• Influence Flower
• Pass@1
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →