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arXiv · 2507.06196

UQLM: A Python Package for Uncertainty Quantification in Large Language Models

Dylan Bouchard·Mohit Singh Chauhan·David Skarbrevik·Ho-Kyeong Ra·Viren Bajaj·Zeya Ahmad
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Abstract

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 downstream applications. We introduce UQLM, a Python package for LLM hallucination detection using state-of-the-art uncertainty quantification (UQ) techniques. This toolkit offers a suite of UQ-based scorers that compute response-level confidence scores ranging from 0 to 1. This library provides an off-the-shelf solution for UQ-based hallucination detection that can be easily integrated to enhance the reliability of LLM outputs.

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Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Ho-Kyeong Ra, Viren Bajaj, Zeya Ahmad. 2025-07-08. UQLM: A Python Package for Uncertainty Quantification in Large Language Models. https://arxiv.org/abs/2507.06196

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