A Survey of Uncertainty Estimation Methods on Large Language Models

• URL: http://arxiv.org/abs/2503.00172v1
• Date: Fri, 28 Feb 2025 20:38:39 GMT
• Title: A Survey of Uncertainty Estimation Methods on Large Language Models
• Authors: Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu,
• Abstract summary: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks.<n>These models could offer biased, hallucinated, or non-factual responses camouflaged by their fluency and realistic appearance.<n>Uncertainty estimation is the key method to address this challenge.
• Score: 12.268958536971782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, these models could offer biased, hallucinated, or non-factual responses camouflaged by their fluency and realistic appearance. Uncertainty estimation is the key method to address this challenge. While research efforts in uncertainty estimation are ramping up, there is a lack of comprehensive and dedicated surveys on LLM uncertainty estimation. This survey presents four major avenues of LLM uncertainty estimation. Furthermore, we perform extensive experimental evaluations across multiple methods and datasets. At last, we provide critical and promising future directions for LLM uncertainty estimation.

Related papers

• Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review [11.856357456956351]
  Large Language Models (LLMs) have been transformative across many domains. Uncertainty Quantification (UQ) to measure uncertainty and employed calibration techniques to address misalignment between uncertainty and accuracy. This survey is the first dedicated study to review the calibration methods and relevant metrics for LLMs.
  arXiv  Detail & Related papers  (2025-04-25T13:34:40Z)
• A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice [7.687545159131024]
  We clarify the definitions of uncertainty and confidence, highlighting their distinctions and implications for model predictions. We categorize various classes of uncertainty estimation methods derived from approaches. We also explore techniques for uncertainty into diverse applications, including out-of-distribution detection, data annotation, and question clarification.
  arXiv  Detail & Related papers  (2024-10-20T07:55:44Z)
• CLUE: Concept-Level Uncertainty Estimation for Large Language Models [49.92690111618016]
  We propose a novel framework for Concept-Level Uncertainty Estimation for Large Language Models (LLMs) We leverage LLMs to convert output sequences into concept-level representations, breaking down sequences into individual concepts and measuring the uncertainty of each concept separately. We conduct experiments to demonstrate that CLUE can provide more interpretable uncertainty estimation results compared with sentence-level uncertainty.
  arXiv  Detail & Related papers  (2024-09-04T18:27:12Z)
• MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty [10.154013836043816]
  We propose a new Multi-Answer Question Answering dataset, MAQA, consisting of world knowledge, mathematical reasoning, and commonsense reasoning tasks. Our findings show that entropy and consistency-based methods estimate the model uncertainty well even under data uncertainty. We believe our observations will pave the way for future work on uncertainty quantification in realistic setting.
  arXiv  Detail & Related papers  (2024-08-13T11:17:31Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• 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)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• One step closer to unbiased aleatoric uncertainty estimation [71.55174353766289]
  We propose a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
  arXiv  Detail & Related papers  (2023-12-16T14:59:11Z)
• 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)
• A Survey of Confidence Estimation and Calibration in Large Language Models [86.692994151323]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks in various domains. Despite their impressive performance, they can be unreliable due to factual errors in their generations. Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
  arXiv  Detail & Related papers  (2023-11-14T16:43:29Z)
• Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models [15.735715641327836]
  We study the risk assessment of Large Language Models (LLMs) from the lens of uncertainty.<n>Our findings validate the effectiveness of uncertainty estimation for revealing LLMs' uncertain/non-factual predictions.<n>Insights from our study shed light on future design and development for reliable LLMs.
  arXiv  Detail & Related papers  (2023-07-16T08:28:04Z)

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.