Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
arXiv:2605.28500v1 Announce Type: cross Abstract: Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerged as a promising approach for detecting hallucinations in natural language generation, but their effectiveness for code generation tasks remains underexplored. We systematically evaluate how UQ techniques transfer to code generation across three programming languages, five LLMs, and over 1,700 problems. We find that some token-probability-based methods generalize effe
The rapid advancement in large language model capabilities for code generation necessitates robust methods for ensuring correctness, especially as these models are integrated into production environments.
Improving the functional correctness and reliability of LLM-generated code is critical for the widespread adoption of AI in software development, reducing debugging overhead and security risks.
The ability to predict functional correctness allows developers to better trust and integrate AI-generated code, potentially accelerating development cycles and the deployment of AI agents.
- · Software developers
- · AI platform providers
- · Cybersecurity firms
- · LLM developers
- · Traditional software testing firms
- · Companies with low code quality standards
Increased reliability and adoption of AI-driven code generation tools.
Reduced incidence of bugs and security vulnerabilities in AI-generated software, freeing up human developers for higher-level tasks.
Accelerated development of complex software, potentially leading to faster innovation in other tech sectors that rely on robust code.
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Read at arXiv cs.LG