3 papers
cs.CL2026
Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
Dylan Bouchard, Mohit Singh Chauhan, Zeya Ahmad +1
Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerge…
cs.CL2026
UQLM: A Python Package for Uncertainty Quantification in Large Language Models
Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik +3
Hallucinations, defined as instances where Large Language Models (LLMs) generate false or misleading content, pose a significant challenge that impacts the safety and trust of down…
cs.CL2025
LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik +2
Large Language Models (LLMs) have been observed to exhibit bias in numerous ways, potentially creating or worsening outcomes for specific groups identified by protected attributes…