3 papers
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.LG2026
TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation
Henrijs Princis, Arindam Sharma, Cristina David
Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when guided only through natural lang…
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…