3 papers
cs.CL2026
Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
Dylan Bouchard, Mohit Singh Chauhan, Viren Bajaj +1
Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are largely designed for short-form outputs an…
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…