# A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads for Hallucination Detection in LLM Outputs

arXiv: https://arxiv.org/abs/2505.08200 · HTML: https://mlsanity.com/paper/2505.08200/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Language Models (Mostly) Know What They Know](https://mlsanity.com/paper/2207.05221/) (2022)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback](https://mlsanity.com/paper/2305.14975/) (2023)
- [The Internal State of an LLM Knows When It's Lying](https://mlsanity.com/paper/2304.13734/) (2023)
- [A Survey of Confidence Estimation and Calibration in Large Language Models](https://mlsanity.com/paper/2311.08298/) (2023)
- [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://mlsanity.com/paper/2209.15558/) (2022)
- [Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://mlsanity.com/paper/2403.04696/) (2024)
- [Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://mlsanity.com/paper/2407.07071/) (2024)
- [LM-Polygraph: Uncertainty Estimation for Language Models](https://mlsanity.com/paper/2311.07383/) (2023)
- [Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph](https://mlsanity.com/paper/2406.15627/) (2024)
- [Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus](https://mlsanity.com/paper/2311.13230/) (2023)
- [LUQ: Long-text Uncertainty Quantification for LLMs](https://mlsanity.com/paper/2403.20279/) (2024)
- [Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models](https://mlsanity.com/paper/2309.15098/) (2023)
- [Do Androids Know They're Only Dreaming of Electric Sheep?](https://mlsanity.com/paper/2312.17249/) (2023)
- [Uncertainty in Natural Language Generation: From Theory to Applications](https://mlsanity.com/paper/2307.15703/) (2023)
- [The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models](https://mlsanity.com/paper/2310.11877/) (2023)
- [Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://mlsanity.com/paper/2407.04121/) (2024)
- [Atomic Calibration of LLMs in Long-Form Generations](https://mlsanity.com/paper/2410.13246/) (2024)
- [Mitigating Uncertainty in Document Classification](https://mlsanity.com/paper/1907.07590/) (2019)
- [Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission](https://mlsanity.com/paper/2311.08726/) (2023)

## Built on by (most cited first, 21 of 21)

- [HaluMem: Evaluating Hallucinations in Memory Systems of Agents](https://mlsanity.com/paper/2511.03506/) (2025)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [Confidence Estimation for LLMs in Multi-turn Interactions](https://mlsanity.com/paper/2601.02179/) (2026)
- [On the Fundamental Impossibility of Hallucination Control in Large Language Models](https://mlsanity.com/paper/2506.06382/) (2025)
- [Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations](https://mlsanity.com/paper/2601.08064/) (2026)
- [From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models](https://mlsanity.com/paper/2606.27679/) (2026)
- [CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals?](https://mlsanity.com/paper/2607.04029/) (2026)
- [Probing for Knowledge Attribution in Large Language Models](https://mlsanity.com/paper/2602.22787/) (2026)
- [Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval](https://mlsanity.com/paper/2603.05471/) (2026)
- [A Robust Evaluation of Probe Robustness: Lessons for Reliable OOD Uncertainty Quantification](https://mlsanity.com/paper/2604.11662/) (2026)
- [Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models](https://mlsanity.com/paper/2604.13991/) (2026)
- [Learning Uncertainty from Sequential Internal Dispersion in Large Language Models](https://mlsanity.com/paper/2604.15741/) (2026)
- [Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models](https://mlsanity.com/paper/2605.31220/) (2026)
- [Failing to See or Failing to Know? Attributing Errors in Vision-Language Models](https://mlsanity.com/paper/2607.04683/) (2026)
- [Fact-Checking with Large Language Models via Probabilistic Certainty and Consistency](https://mlsanity.com/paper/2601.02574/) (2026)
- [From Retinal Evidence to Safe Decisions: RETINA-SAFE and ECRT for Hallucination Risk Triage in Medical LLMs](https://mlsanity.com/paper/2604.05348/) (2026)
- [DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations](https://mlsanity.com/paper/2606.02289/) (2026)
- [Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification](https://mlsanity.com/paper/2607.16868/) (2026)
- [Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models](https://mlsanity.com/paper/2609.24881/) (2026)
- [ChainUQ: Reasoning Consistency-Aware Uncertainty Quantification for Large Language Models](https://mlsanity.com/paper/2609.26060/) (2026)
- [Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models](https://mlsanity.com/paper/2610.03482/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have the tendency to hallucinate, i.e., to sporadically generate false or fabricated information. This presents a major challenge, as hallucinations often appear highly convincing and users generally lack the tools to detect them. Uncertainty quantification (UQ) provides a framework for assessing the reliability of model outputs, aiding in the identification of potential hallucinations. In this work, we introduce pre-trained UQ heads: supervised auxiliary modules for LLMs that substantially enhance their ability to capture uncertainty compared to unsupervised UQ methods. Their strong performance stems from the powerful Transformer architecture in their design and informative features derived from LLM attention maps. Experimental evaluation shows that these heads are highly robust and achieve state-of-the-art performance in claim-level hallucination detection across both in-domain and out-of-domain prompts. Moreover, these modules demonstrate strong generalization to languages they were not explicitly trained on. We pre-train a collection of UQ heads for popular LLM series, including Mistral, Llama, and Gemma 2. We publicly release both the code and the pre-trained heads.