Cover Image

The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tomov, Tim, Fuchsgruber, Dominik, Wollschläger, Tom, Günnemann, Stephan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Task-Aware Calibration: Provably Optimal Decoding in LLMs
by: Tomov, Tim, et al.
Published: (2026)

Task-Awareness Improves LLM Generations and Uncertainty
by: Tomov, Tim, et al.
Published: (2026)

Energy-based Epistemic Uncertainty for Graph Neural Networks
by: Fuchsgruber, Dominik, et al.
Published: (2024)

Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
by: Fuchsgruber, Dominik, et al.
Published: (2025)

Uncertainty for Active Learning on Graphs
by: Fuchsgruber, Dominik, et al.
Published: (2024)

Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance
by: Fuchsgruber, Dominik, et al.
Published: (2024)

The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence
by: Wollschläger, Tom, et al.
Published: (2025)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation
by: Kamel, Rafiq, et al.
Published: (2025)

The Illusion of Stochasticity in LLMs
by: Gu, Xiangming, et al.
Published: (2026)

What Expressivity Theory Misses: Message Passing Complexity for GNNs
by: Kemper, Niklas, et al.
Published: (2025)

The Confidence Trap: Gender Bias and Predictive Certainty in LLMs
by: Sabir, Ahmed, et al.
Published: (2026)

Extracting Unlearned Information from LLMs with Activation Steering
by: Seyitoğlu, Atakan, et al.
Published: (2024)

Localized Randomized Smoothing for Collective Robustness Certification
by: Schuchardt, Jan, et al.
Published: (2022)

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
by: Bakman, Yavuz, et al.
Published: (2025)

Diffusion LLMs are Natural Adversaries for any LLM
by: Lüdke, David, et al.
Published: (2025)

Calibrating Expressions of Certainty
by: Wang, Peiqi, et al.
Published: (2024)

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
by: Zhang, Boxuan, et al.
Published: (2025)

Why Semantic Entropy Fails: Geometry-Aware and Calibrated Uncertainty for Policy Optimization
by: Zhang, Zheyuan, et al.
Published: (2026)

The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
by: Janiak, Denis, et al.
Published: (2025)

Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
by: McCabe, Lucas H., et al.
Published: (2025)

Uncertainty Quantification for In-Context Learning of Large Language Models
by: Ling, Chen, et al.
Published: (2024)

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
by: Nikitin, Alexander, et al.
Published: (2024)

Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
by: Liu, Linyu, et al.
Published: (2024)

MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
by: Liu, Gabrielle Kaili-May, et al.
Published: (2025)

Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity
by: Liang, Kaiqu, et al.
Published: (2024)

Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification
by: Menendez, Angel Rodrigo Avelar, et al.
Published: (2026)

To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
by: Sedova, Anastasiia, et al.
Published: (2024)

Ambiguity in LLMs is a concept missing problem
by: Hu, Zhibo, et al.
Published: (2025)

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
by: Lin, Zhen, et al.
Published: (2023)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
by: Vashurin, Roman, et al.
Published: (2024)

Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
by: Sahoo, Subramanyam
Published: (2026)

Interpretability Illusions in the Generalization of Simplified Models
by: Friedman, Dan, et al.
Published: (2023)

Expressivity and Generalization: Fragment-Biases for Molecular GNNs
by: Wollschläger, Tom, et al.
Published: (2024)

Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
by: Schwinn, Leo, et al.
Published: (2025)

The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
by: Han, Pengrui, et al.
Published: (2025)

Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
by: Nguyen, Dang, et al.
Published: (2025)

Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
by: Mao, Zhenjiang, et al.
Published: (2026)

DiscoUQ: Structured Disagreement Analysis for Uncertainty Quantification in LLM Agent Ensembles
by: Jiang, Bo
Published: (2026)

Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
by: Liu, Shang, et al.
Published: (2024)