4 citations · 5 across the 10 of their papers we have counts for
10 papers
When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study
Mohit Singh Chauhan, Vipin Gyanchandani, Dylan Bouchard
Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where ground-truth evidence is unavailab…
Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents
Dylan Bouchard, Mohit Singh Chauhan
Uncertainty quantification (UQ) methods for language models are typically evaluated on single-turn outputs, where uncertainty is attached to one generated answer. For LLM agents, h…
DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations
Mohit Singh Chauhan
Existing hallucination taxonomies classify LLM errors by what is wrong with the output -- memorised misconceptions, reasoning failures, fluent fabrications -- but cannot answer a d…
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…
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…
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…
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
Dylan Bouchard, Mohit Singh Chauhan
Hallucinations are a persistent problem with Large Language Models (LLMs). As these models become increasingly used in high-stakes domains, such as healthcare and finance, the need…
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…
On Active Learning for Gaussian Process-based Global Sensitivity Analysis
Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli +3
This paper explores the application of active learning strategies to adaptively learn Sobol indices for global sensitivity analysis. We demonstrate that active learning for Sobol i…
UQpy v4.1: Uncertainty Quantification with Python
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis +9
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured t…