Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification

Researchers demonstrate that uncertainty quantification (UQ) methods can effectively detect errors in LLM-generated code by introducing functional equivalence techniques. While token-probability methods transfer well from NLP, sampling-based approaches fail because traditional semantic models cannot distinguish functionally different code. The proposed functional entropy method outperforms existing approaches across most benchmarks.

This research addresses a critical gap in AI reliability: while large language models excel at code generation, their outputs frequently contain functional errors that traditional verification methods struggle to catch. The study evaluates how uncertainty quantification—techniques proven effective in detecting hallucinations in natural language—perform when applied to code generation tasks across multiple programming languages and models.

The core finding reveals a fundamental limitation: natural language inference models, which work well for semantic equivalence in text, cannot distinguish between code that looks similar but behaves differently. This causes sampling-based methods to collapse responses into single clusters, rendering them ineffective. Rather than forcing NLP techniques onto code, the researchers developed functional equivalence methods that leverage LLMs themselves to assess whether generated code variants produce identical outputs.

This breakthrough has substantial implications for AI development infrastructure. As organizations increasingly rely on LLM-generated code for production systems, robust error detection becomes essential. Functional entropy and related techniques enable developers to quantify confidence in generated code without requiring expensive test-case execution. This could significantly reduce debugging cycles and improve deployment safety.

The consistent outperformance across 15 model-benchmark combinations suggests these methods generalize well. However, scalability remains unexplored—applying functional equivalence to large codebases or resource-constrained environments requires further investigation. The work establishes a promising foundation for code-specific uncertainty quantification, potentially influencing how AI-assisted development tools assess output reliability.

• →Functional entropy, a code-specific uncertainty metric, outperforms NLP-based semantic equivalence methods across 11 of 15 tested model-benchmark combinations.
• →Traditional NLI models fail at detecting functional differences in code, causing sampling-based uncertainty methods to collapse into single semantic clusters.
• →Token-probability-based UQ methods transfer effectively to code generation without modification, providing a baseline alternative.
• →LLM-based functional equivalence assessment can replace natural language inference for code verification tasks.
• →The research evaluates 5 LLMs across 3 programming languages on 1,700+ problems, demonstrating broad applicability.