UQLM: A Python Package for Uncertainty Quantification in Large Language Models
Summary
Hallucinations (instances where Large Language Models generate false or misleading content) are a safety problem for AI applications. The paper introduces UQLM, a Python package that uses uncertainty quantification (UQ, a statistical technique for measuring how confident a model is in its answer) to detect when an LLM is likely hallucinating by assigning confidence scores between 0 and 1 to responses.
Solution / Mitigation
The source describes UQLM as 'an off-the-shelf solution for UQ-based hallucination detection that can be easily integrated to enhance the reliability of LLM outputs.' No specific implementation steps, code examples, or version details are provided in the source text.
Classification
Original source: http://jmlr.org/papers/v27/25-1557.html
First tracked: March 16, 2026 at 04:11 PM
Classified by LLM (prompt v3) · confidence: 92%