# Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation

arXiv: https://arxiv.org/abs/2502.11620 · HTML: https://mlsanity.com/paper/2502.11620/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2025.

## Built on by (most cited first, 14 of 14)

- [On LLMs' Internal Representation of Code Correctness](https://mlsanity.com/paper/2512.07404/) (2025)
- [The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion](https://mlsanity.com/paper/2508.16131/) (2025)
- [INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models](https://mlsanity.com/paper/2510.01389/) (2025)
- [Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering](https://mlsanity.com/paper/2506.11021/) (2025)
- [EGG-SR: Embedding Symbolic Equivalence into Symbolic Regression via Equality Graph](https://mlsanity.com/paper/2511.05849/) (2025)
- [Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics](https://mlsanity.com/paper/2601.02200/) (2026)
- [CONCUR: Benchmarking LLMs for Concurrent Code Generation](https://mlsanity.com/paper/2603.03683/) (2026)
- [Heteroskedastic Signals in Budgeted LLM Verification: Structural Heterogeneity Limits Optimization Gains](https://mlsanity.com/paper/2606.15841/) (2026)
- [LLMs + Security = Trouble](https://mlsanity.com/paper/2602.08422/) (2026)
- [Inference-Time Code Selection via Symbolic Equivalence Partitioning](https://mlsanity.com/paper/2604.06485/) (2026)
- [Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs](https://mlsanity.com/paper/2607.12273/) (2026)
- [Is This Your Final Answer? Cross-Contextual Consistency as a Measure of LLM Credibility](https://mlsanity.com/paper/2608.10315/) (2026)
- [When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation](https://mlsanity.com/paper/2608.14659/) (2026)
- [Characterizing Overconfident Failure in LLM-Based Code Generation](https://mlsanity.com/paper/2610.11300/) (2026)

## Abstract (arXiv)

In this work, we explore uncertainty estimation as a proxy for correctness in LLM-generated code. To this end, we adapt two state-of-the-art techniques from natural language generation -- one based on entropy and another on mutual information -- to the domain of code generation. Given the distinct semantic properties of code, we introduce modifications, including a semantic equivalence check based on symbolic execution. Our findings indicate a strong correlation between the uncertainty computed through these techniques and correctness, highlighting the potential of uncertainty estimation for quality assessment. Additionally, we propose a simplified version of the entropy-based method that assumes a uniform distribution over the LLM's responses, demonstrating comparable effectiveness. Using these techniques, we develop an abstention policy that prevents the model from making predictions when uncertainty is high, reducing incorrect outputs to near zero. Our evaluation on the LiveCodeBench shows that our approach significantly outperforms a baseline relying solely on LLM-reported log-probabilities.