arXiv AI
Jul 7

The Anatomy of Uncertainty in LLMs

arXiv:2603. 24967v2 Announce Type: replace Abstract: Understanding why a large language model (LLM) is uncertain about the response is important for their reliable deployment.

By Aditya Taparia, Ransalu Senanayake, Kowshik Thopalli, Vivek Narayanaswamy
Hugging Face Trending Papers
Jun 22

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.

arXiv AI
Jul 23

Rethinking Uncertainty Evaluation in Large Language Models

arXiv:2607. 19367v1 Announce Type: new Abstract: Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depends on the evaluation distribution, and does not test the extent to which the estimation can be interpreted as a consistent, underlying probability function.

By Krish Matta, Atharv Naphade, Andy Zou
arXiv Machine Learning
Jun 11

What Uncertainties Do We Need for Dynamical Systems?

arXiv:2606. 11988v1 Announce Type: new Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling.

By Yusuf Sale, Christopher B\"ulte, Felix Czaja, Joshua Stiller, Eyke H\"ullermeier
arXiv Machine Learning
1d ago

Language models suffer from a curse of ambiguity

arXiv:2608. 15448v1 Announce Type: cross Abstract: Large language models increasingly rely on sampling as a driver of their own improvement, making the fidelity of their learned distributions more critical than ever.

By Nicolas Zucchet, Hyun Dong Lee, Scott Linderman
arXiv Machine Learning
Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee