Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs

• URL: http://arxiv.org/abs/2506.07448v1
• Date: Mon, 09 Jun 2025 05:52:03 GMT
• Title: Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs
• Authors: T. Duy Nguyen-Hien, Desi R. Ivanova, Yee Whye Teh, Wee Sun Lee,
• Abstract summary: We advocate for the adoption of a framework that provides a coherent foundation to reason about uncertainty and clarify the reducibility of uncertainty.<n>By supporting active resolution rather than passive avoidance, it opens the door to more reliable, transparent, and broadly applicable LLM systems.
• Score: 40.7342896954488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although large language models (LLMs) are highly interactive and extendable, current approaches to ensure reliability in deployments remain mostly limited to rejecting outputs with high uncertainty in order to avoid misinformation. This conservative strategy reflects the current lack of tools to systematically distinguish and respond to different sources of uncertainty. In this paper, we advocate for the adoption of Bayesian Modeling of Experiments -- a framework that provides a coherent foundation to reason about uncertainty and clarify the reducibility of uncertainty -- for managing and proactively addressing uncertainty that arises in LLM deployments. This framework enables LLMs and their users to take contextually appropriate steps, such as requesting clarification, retrieving external information, or refining inputs. By supporting active resolution rather than passive avoidance, it opens the door to more reliable, transparent, and broadly applicable LLM systems, particularly in high-stakes, real-world settings.

Related papers

• Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling [48.15636223774418]
  Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness.<n>We propose the Explicit Knowledge Boundary Modeling framework to integrate fast and slow reasoning systems to harmonize reliability and usability.
  arXiv  Detail & Related papers  (2025-03-04T03:16:02Z)
• Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses [4.505944978127014]
  We introduce a multi-dimensional UQ framework that integrates semantic and knowledge-aware similarity analysis.<n>This approach disentangles overlapping information from both semantic and knowledge dimensions, capturing both semantic variations and factual consistency.<n>Our empirical evaluations demonstrate that our method outperforms existing techniques in identifying uncertain responses.
  arXiv  Detail & Related papers  (2025-02-24T04:05:08Z)
• Drawing the Line: Enhancing Trustworthiness of MLLMs Through the Power of Refusal [21.342265570934995]
  Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability.<n>We present the Information Boundary-aware Learning Framework (InBoL), a novel approach that empowers MLLMs to refuse to answer user queries when encountering insufficient information.<n>This framework introduces a comprehensive data generation pipeline and tailored training strategies to improve the model's ability to deliver appropriate refusal responses.
  arXiv  Detail & Related papers  (2024-12-15T14:17:14Z)
• 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)
• Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings [11.33157177182775]
  Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria. We propose a novel approach that leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty.
  arXiv  Detail & Related papers  (2024-10-30T04:41:46Z)
• 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.<n>This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt.<n>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)
• 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)
• Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space [14.715989394285238]
  Existing Large Language Models (LLMs) do not have an inherent functionality to provide the users with an uncertainty/confidence metric for each response it generates. A new framework is proposed in this paper to address these issues. Semantic density extracts uncertainty/confidence information for each response from a probability distribution perspective in semantic space.
  arXiv  Detail & Related papers  (2024-05-22T17:13:49Z)
• 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.