4 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Is Escalation Worth It? On the Depth of LLM Cascades
Dylan Bouchard
LLM cascades, in which a cheap model defers to an expensive one on low-confidence queries, are widely used to reduce inference cost. Given a pool of models, a practitioner must dec…
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…