uqlm
UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection
Star Growth
Overview
UQLM (Uncertainty Quantification for Language Models) is a Python library designed to detect hallucinations in Large Language Model outputs using advanced uncertainty quantification techniques. Developed by CVS Health and backed by peer-reviewed research published in JMLR and TMLR, this tool addresses one of the most critical challenges in deploying LLMs in production: ensuring reliability and detecting when models generate incorrect or fabricated information. The library provides a comprehensive suite of response-level scorers that analyze LLM outputs and return confidence scores between 0 and 1, where higher scores indicate lower likelihood of hallucinations or errors. UQLM categorizes different scorer types based on their latency, cost, and compatibility characteristics, allowing users to choose the appropriate method based on their specific requirements and constraints. This flexibility makes it suitable for both research applications and production deployments where reliability is paramount. The tool's academic foundation ensures that the uncertainty quantification methods are scientifically validated, while its practical design allows for seamless integration into existing LLM workflows. With over 1,100 GitHub stars, UQLM has gained recognition in the AI community as a reliable solution for improving LLM trustworthiness.
Deep Analysis
Academically rigorous uncertainty quantification (published in JMLR/TMLR) with the broadest scorer variety — unlike guardrails tools that use simple heuristics, UQLM applies information-theoretic methods like semantic entropy for precise hallucination detection
⚡ Capabilities
- • LLM hallucination detection via uncertainty quantification
- • Black-box scorers (consistency-based, semantic entropy)
- • White-box scorers (token probability-based)
- • LLM-as-a-Judge scoring
- • Ensemble scorers combining multiple methods
- • Long-text claim-level uncertainty scoring
- • Automatic uncertainty-minimized response selection
🔗 Integrations
✓ Best For
- ✓ Adding hallucination detection to existing LLM applications
- ✓ Research teams studying LLM uncertainty and reliability
✗ Not Ideal For
- ✗ Teams needing a full guardrails/safety platform (use NeMo Guardrails)
- ✗ Real-time low-latency applications where multiple LLM calls are prohibitive
Languages
Deployment
Pricing Detail
⚠ Known Limitations
- ⚠ Black-box methods require multiple LLM calls, increasing cost and latency
- ⚠ White-box methods limited to models exposing token probabilities
- ⚠ Research-focused library — less production infrastructure than guardrail platforms
- ⚠ Async-first API may require adaptation for synchronous codebases
Pros
- + Research-backed uncertainty quantification methods published in top-tier academic journals (JMLR, TMLR)
- + Multiple scorer types offering different trade-offs between latency, cost, and accuracy for flexible deployment
- + Simple installation and integration with existing LLM workflows through PyPI distribution
Cons
- - Requires Python 3.10+ which may limit compatibility with older environments
- - Different scorers add varying levels of latency and computational cost to LLM inference
- - Limited to response-level scoring rather than token-level or real-time uncertainty detection
Use Cases
- • Production LLM applications requiring confidence scores to filter or flag potentially unreliable outputs
- • Research and development of hallucination detection systems and uncertainty quantification methods
- • Quality assurance workflows for LLM-generated content in critical domains like healthcare or finance