Towards Harmonized Uncertainty Estimation for Large Language Models

• URL: http://arxiv.org/abs/2505.19073v2
• Date: Sun, 20 Jul 2025 15:35:43 GMT
• Title: Towards Harmonized Uncertainty Estimation for Large Language Models
• Authors: Rui Li, Jing Long, Muge Qi, Heming Xia, Lei Sha, Peiyi Wang, Zhifang Sui,
• Abstract summary: It is essential to quantify the reliability of their generations through uncertainty estimation.<n>We propose CUE (Corrector for Uncertainty Estimation): A straightforward yet effective method that employs a lightweight model trained on data aligned with the target LLM's performance to adjust uncertainty scores.
• Score: 22.58034272573749
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To facilitate robust and trustworthy deployment of large language models (LLMs), it is essential to quantify the reliability of their generations through uncertainty estimation. While recent efforts have made significant advancements by leveraging the internal logic and linguistic features of LLMs to estimate uncertainty scores, our empirical analysis highlights the pitfalls of these methods to strike a harmonized estimation between indication, balance, and calibration, which hinders their broader capability for accurate uncertainty estimation. To address this challenge, we propose CUE (Corrector for Uncertainty Estimation): A straightforward yet effective method that employs a lightweight model trained on data aligned with the target LLM's performance to adjust uncertainty scores. Comprehensive experiments across diverse models and tasks demonstrate its effectiveness, which achieves consistent improvements of up to 60% over existing methods.

Related papers

• Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations [49.84786015324238]
  Confidence estimation (CE) indicates how reliable the answers of large language models (LLMs) are, and can impact user trust and decision-making.<n>We present a comprehensive evaluation framework for CE that measures their confidence quality on three new aspects.<n>These include robustness of confidence against prompt perturbations, stability across semantic equivalent answers, and sensitivity to semantically different answers.
  arXiv  Detail & Related papers  (2026-01-12T23:16:50Z)
• ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models [23.44710972442814]
  Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet the uncertainty of Large Language Models (LLMs) is non-trivial.<n>We propose a novel grey-box uncertainty quantification method that measures the variation in model outputs before and after semantic-preserving intervention.
  arXiv  Detail & Related papers  (2025-10-15T02:46:43Z)
• Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation [68.106428321492]
  Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding.<n>LLMs hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans.<n>We present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately.
  arXiv  Detail & Related papers  (2025-10-09T10:26:58Z)
• Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation [20.726685669562496]
  Hallucinations are a common issue that undermine the reliability of large language models (LLMs)<n>Recent studies have identified a subset of hallucinations, known as confabulations, which arise due to predictive uncertainty of LLMs.<n>To detect confabulations, various methods for estimating predictive uncertainty in natural language generation (NLG) have been developed.
  arXiv  Detail & Related papers  (2025-10-02T17:54:09Z)
• Can Large Language Models Express Uncertainty Like Human? [71.27418419522884]
  We release the first diverse, large-scale dataset of hedging expressions with human-annotated confidence scores.<n>We conduct the first systematic study of linguistic confidence across modern large language models.
  arXiv  Detail & Related papers  (2025-09-29T02:34:30Z)
• Revisiting Uncertainty Estimation and Calibration of Large Language Models [28.493449764136518]
  We present the most comprehensive study to date of uncertainty estimation in large language models (LLMs)<n>We focus on three representative black-box single-pass methods, including token probability-based uncertainty (TPU), numerical verbal uncertainty (NVU) and linguistic verbal uncertainty (LVU)<n>Our results show that LVU consistently outperforms TPU and NVU, offering stronger calibration and discrimination while being more interpretable.
  arXiv  Detail & Related papers  (2025-05-29T02:04:49Z)
• Token-Level Uncertainty Estimation for Large Language Model Reasoning [24.56760223952017]
  Large Language Models (LLMs) have demonstrated impressive capabilities, but their output quality remains inconsistent across various application scenarios.<n>We propose a token-level uncertainty estimation framework to enable LLMs to self-assess and self-improve their generation quality in mathematical reasoning.
  arXiv  Detail & Related papers  (2025-05-16T22:47:32Z)
• Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection [4.151658495779136]
  Large language models (LLMs) often generate factually incorrect outputs, known as hallucinations.<n>We present a systematic framework for decomposing uncertainty into four distinct sources.<n>We propose a method for task specific metric/model selection guided by the alignment or divergence between their uncertainty characteristics and that of a given task.
  arXiv  Detail & Related papers  (2025-05-12T07:55:22Z)
• Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling [48.15636223774418]
  Large language models (LLMs) frequently hallucinate due to misaligned self-awareness.<n>Existing approaches mitigate hallucinations via uncertainty estimation or query rejection.<n>We propose the Explicit Knowledge Boundary Modeling framework to integrate fast and slow reasoning systems.
  arXiv  Detail & Related papers  (2025-03-04T03:16:02Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning [10.457661605916435]
  Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities.<n>LLMs are sometimes prone to generating credible-sounding but incorrect information, a phenomenon known as hallucinations.<n>We introduce a novel uncertainty-aware causal language modeling loss function, grounded in the principles of decision theory.
  arXiv  Detail & Related papers  (2024-12-03T23:14:47Z)
• SAUP: Situation Awareness Uncertainty Propagation on LLM Agent [52.444674213316574]
  Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications.<n>Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multistep decision-making process and the dynamic interactions between agents and their environments.<n>We propose SAUP, a novel framework that propagates uncertainty through each step of an LLM-based agent's reasoning process.
  arXiv  Detail & Related papers  (2024-12-02T01:31:13Z)
• Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework [54.40508478482667]
  We present a comprehensive framework to disentangle, quantify, and mitigate uncertainty in perception and plan generation.<n>We propose methods tailored to the unique properties of perception and decision-making.<n>We show that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines.
  arXiv  Detail & Related papers  (2024-11-03T17:32:00Z)
• Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models [96.43562963756975]
  We train a regression model, which target variable is the gap between the conditional and the unconditional generation confidence. We use this learned conditional dependency model to modulate the uncertainty of the current generation step based on the uncertainty of the previous step.
  arXiv  Detail & Related papers  (2024-08-20T09:42:26Z)
• Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach [6.209293868095268]
  We study the problem of uncertainty estimation and calibration for LLMs. We propose a supervised approach that leverages labeled datasets to estimate the uncertainty in LLMs' responses. Our method is easy to implement and adaptable to different levels of model accessibility including black box, grey box, and white box.
  arXiv  Detail & Related papers  (2024-04-24T17:10:35Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.