mlsanityData to 14 Oct 2026

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

arXiv 2507.06196 ↗First posted 8 Jul 2025cs.CL cs.AI cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 8 Jul 2025.

Builds on

18 of 24 references.

References · 18

  1. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  3. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  4. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 819
  5. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  6. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  7. 2023Generating with Confidence: Uncertainty Quantification for Black-box Large Language Modelscited by 293
  8. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 288
  9. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 251
  10. 2023LM-Polygraph: Uncertainty Estimation for Language Modelscited by 131
  11. 2023Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthinesscited by 122
  12. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
6 more
  1. 2024Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraphcited by 105
  2. 2023Selectively Answering Ambiguous Questionscited by 66
  3. 2025Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistencycited by 30
  4. 2024RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Modelscited by 25
  5. 2025Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorerscited by 18
  6. 2024RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Sciencecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.

  1. 2025Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoningcited by 10
  2. 2026Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Studycited by 4
  3. 2026Is Escalation Worth It? On the Depth of LLM Cascadescited by 4
  4. 2026Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Trainingcited by 1
  5. 2026Sanity Checks for Long-Form Hallucination Detectioncited by 1
  6. 2026Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimationcited by 1
  7. 2025Unsupervised Evaluation of Multi-Turn Objective-Driven Interactionscited by 0
  8. 2026Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarkscited by 0
  9. 2026How Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?cited by 0