A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

• URL: http://arxiv.org/abs/2412.05563v2
• Date: Tue, 01 Jul 2025 22:08:39 GMT
• Title: A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
• Authors: Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar,
• Abstract summary: Large language models (LLMs) generate plausible, factually-incorrect responses, which are expressed with striking confidence.<n>Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt.<n>This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses.
• Score: 9.045698110081686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect responses, which are expressed with striking confidence. Previous work has shown that hallucinations and other non-factual responses generated by LLMs can be detected by examining the uncertainty of the LLM in its response to the pertinent prompt, driving significant research efforts devoted to quantifying the uncertainty of LLMs. This survey seeks to provide an extensive review of existing uncertainty quantification methods for LLMs, identifying their salient features, along with their strengths and weaknesses. We present existing methods within a relevant taxonomy, unifying ostensibly disparate methods to aid understanding of the state of the art. Furthermore, we highlight applications of uncertainty quantification methods for LLMs, spanning chatbot and textual applications to embodied artificial intelligence applications in robotics. We conclude with open research challenges in uncertainty quantification of LLMs, seeking to motivate future research.

Related papers

• Robust Uncertainty Quantification for Factual Generation of Large Language Models [22.060021788289202]
  Large language model(LLM) technology has facilitated its integration into various domains of professional and daily life.<n>The persistent challenge of LLM hallucination has emerged as a critical limitation, significantly compromising the reliability and trustworthiness of AI-generated content.<n>This study proposes an uncertainty quantification scenario in the task of generating with multiple facts.
  arXiv  Detail & Related papers  (2026-01-01T14:06:58Z)
• Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions [28.64896454455385]
  Large language models (LLMs) remain prone to hallucinations that produce plausible but factually incorrect outputs.<n>Uncertainty quantification (UQ) has emerged as a central research direction to address this issue.<n>We examine the role of UQ in hallucination detection, where quantifying uncertainty provides a mechanism for identifying unreliable generations.
  arXiv  Detail & Related papers  (2025-10-14T00:49:04Z)
• Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty? [11.309445539128733]
  Large language models (LLMs) have delivered significant breakthroughs across diverse domains but can still produce unreliable or misleading outputs.<n>We show that, when an LLM is uncertain, the patterns of disagreement among its multiple generated responses contain rich clues about the underlying cause of uncertainty.
  arXiv  Detail & Related papers  (2025-08-28T20:14:35Z)
• ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering [3.131352561462676]
  Large Language Models (LLMs) have contributed to the development of AI systems capable of factual question-answering.<n>No known study tests the LLMs' robustness when presented with obfuscated versions of questions.<n>We introduce ObfusQA, a framework with multi-tiered obfuscation levels to examine LLM capabilities.
  arXiv  Detail & Related papers  (2025-08-10T12:27:52Z)
• Representations of Fact, Fiction and Forecast in Large Language Models: Epistemics and Attitudes [15.754908203866284]
  Rational speakers are supposed to know what they know and what they do not know.<n>It is still a challenge for current large language models to generate corresponding utterances based on the assessment of facts and confidence in an uncertain real-world environment.
  arXiv  Detail & Related papers  (2025-06-02T10:19:42Z)
• Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey [54.90240495777929]
  Ambiguity remains a fundamental challenge in Natural Language Processing (NLP)<n>With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications.<n>This paper explores the definition, forms, and implications of ambiguity for language driven systems.
  arXiv  Detail & Related papers  (2025-05-18T20:53:41Z)
• UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models [41.67393607081513]
  Large Language Models (LLMs) often struggle to accurately express the factual knowledge they possess. We propose the UAlign framework, which leverages Uncertainty estimations to represent knowledge boundaries. We show that the proposed UAlign can significantly enhance the LLMs' capacities to confidently answer known questions.
  arXiv  Detail & Related papers  (2024-12-16T14:14:27Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• Defining Boundaries: A Spectrum of Task Feasibility for Large Language Models [6.008311204104302]
  Large language models (LLMs) have shown remarkable performance in various tasks but often fail to handle queries that exceed their knowledge and capabilities. This paper addresses the need for LLMs to recognize and refuse infeasible tasks due to the required skills surpassing their capabilities.
  arXiv  Detail & Related papers  (2024-08-11T22:58:23Z)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
  Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
  arXiv  Detail & Related papers  (2024-07-20T11:19:58Z)
• 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)
• Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration [39.603649838876294]
  We study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. Motivated by their failures in self-reflection and over-reliance on held-out sets, we propose two novel approaches.
  arXiv  Detail & Related papers  (2024-02-01T06:11:49Z)
• Benchmarking LLMs via Uncertainty Quantification [91.72588235407379]
  The proliferation of open-source Large Language Models (LLMs) has highlighted the urgent need for comprehensive evaluation methods. We introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs.
  arXiv  Detail & Related papers  (2024-01-23T14:29:17Z)
• Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge [35.067234242461545]
  Large language models (LLMs) express uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses. This work aims to systematically investigate LLMs' behaviors in such situations, emphasizing the trade-off between honesty and helpfulness.
  arXiv  Detail & Related papers  (2023-11-16T10:02:40Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models [16.524794442035265]
  We study the risk assessment of Large Language Models (LLMs) from the lens of uncertainty. Our findings validate the effectiveness of uncertainty estimation for revealing LLMs' uncertain/non-factual predictions. Insights from our study shed light on future design and development for reliable LLMs.
  arXiv  Detail & Related papers  (2023-07-16T08:28:04Z)
• Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models [27.491408293411734]
  Large Language Models (LLMs) show promising results in language generation and instruction following but frequently "hallucinate" Our research introduces a simple redundancy: not all tokens in auto-regressive text equally represent the underlying meaning.
  arXiv  Detail & Related papers  (2023-07-03T22:17:16Z)

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.