cvs-health/uqlm

Health v2 · maintenance only

Adoption not part of the score

1195 stars · 131 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

UQLM is a Python library for detecting LLM hallucinations using uncertainty quantification techniques. It provides five categories of scorers (black-box, white-box, LLM-as-a-judge, ensemble, and long-text claim-level) that return confidence scores between 0 and 1 for LLM responses.

Use cases

• detect hallucinations in LLM outputs
• score confidence of LLM responses
• quantify uncertainty in large language model generations
• evaluate LLM reliability
• flag unreliable claims in long-form LLM text
• add hallucination detection to an LLM pipeline

When to choose

• you need response-level confidence scores for LLM outputs
• you want multiple scorer types including consistency-based and token-probability methods
• you need claim-level uncertainty for long-form responses
• you want a peer-reviewed, actively maintained Python package

When to avoid

• you need training-time uncertainty methods rather than inference-time scoring
• you cannot tolerate extra LLM calls or latency for black-box or judge scorers
• you need model internals beyond token logprobs

Facets

library · maturity active

machine-learning llm-inference monitoring testing large-language-models machine-learning artificial-intelligence python hallucination-detection uncertainty-quantification confidence-scoring llm-evaluation ai-safety llm-as-a-judge natural-language-processing

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