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cs.SE2026
Using Semantic Distance to Estimate Uncertainty in LLM-Based Code Generation
Weilin He, Arindam Sharma, Cristina David
LLMs show strong performance in code generation, but their outputs lack correctness guarantees. Sample-based uncertainty estimators address this by generating multiple candidate pr…
cs.SE2025
Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation
Arindam Sharma, Cristina David
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 genera…