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

• URL: http://arxiv.org/abs/2507.06196v1
• Date: Tue, 08 Jul 2025 17:22:32 GMT
• Title: UQLM: A Python Package for Uncertainty Quantification in Large Language Models
• Authors: Dylan Bouchard, Mohit Singh Chauhan, David Skarbrevik, Ho-Kyeong Ra, Viren Bajaj, Zeya Ahmad,
• Abstract summary: We introduce UQLM, a Python package for hallucination detection using state-of-the-art uncertainty quantification (UQ) techniques.<n>This toolkit offers a suite of UQ-based scorers that compute response-level confidence scores ranging from 0 to 1.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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