Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents

• URL: http://arxiv.org/abs/2505.22655v1
• Date: Wed, 28 May 2025 17:59:08 GMT
• Title: Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
• Authors: Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci,
• Abstract summary: This position paper argues that this traditional dichotomy of uncertainties is too limited for the open and interactive setup that LLM agents operate in.<n>We propose three novel research directions that focus on uncertainties in such human-computer interactions.
• Score: 19.974114160377045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty quantification plays a crucial role, aiming to quantify the level of ambiguity in either one overall number or two numbers for aleatoric and epistemic uncertainty. This position paper argues that this traditional dichotomy of uncertainties is too limited for the open and interactive setup that LLM agents operate in when communicating with a user, and that we need to research avenues that enrich uncertainties in this novel scenario. We review the literature and find that popular definitions of aleatoric and epistemic uncertainties directly contradict each other and lose their meaning in interactive LLM agent settings. Hence, we propose three novel research directions that focus on uncertainties in such human-computer interactions: Underspecification uncertainties, for when users do not provide all information or define the exact task at the first go, interactive learning, to ask follow-up questions and reduce the uncertainty about the current context, and output uncertainties, to utilize the rich language and speech space to express uncertainties as more than mere numbers. We expect that these new ways of dealing with and communicating uncertainties will lead to LLM agent interactions that are more transparent, trustworthy, and intuitive.

Related papers

• Teaching Language Models To Gather Information Proactively [53.85419549904644]
  Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
  arXiv  Detail & Related papers  (2025-07-28T23:50:09Z)
• Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs [40.7342896954488]
  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.
  arXiv  Detail & Related papers  (2025-06-09T05:52:03Z)
• Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs [13.982395477368396]
  Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge.<n>They are still prone to hallucinations, generating incorrect or misleading information, often accompanied by high uncertainty.<n>We introduce Semantic Volume, a novel measure for quantifying both external and internal uncertainty in LLMs.
  arXiv  Detail & Related papers  (2025-02-28T17:09:08Z)
• 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)
• 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-Based Abstention in LLMs Improves Safety and Reduces Hallucinations [63.330182403615886]
  A major barrier towards the practical deployment of large language models (LLMs) is their lack of reliability. Three situations where this is particularly apparent are correctness, hallucinations when given unanswerable questions, and safety. In all three cases, models should ideally abstain from responding, much like humans, whose ability to understand uncertainty makes us refrain from answering questions we don't know.
  arXiv  Detail & Related papers  (2024-04-16T23:56:38Z)
• Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty [53.336235704123915]
  We investigate how LMs incorporate confidence in responses via natural language and how downstream users behave in response to LM-articulated uncertainties. We find that LMs are reluctant to express uncertainties when answering questions even when they produce incorrect responses. We test the risks of LM overconfidence by conducting human experiments and show that users rely heavily on LM generations. Lastly, we investigate the preference-annotated datasets used in post training alignment and find that humans are biased against texts with uncertainty.
  arXiv  Detail & Related papers  (2024-01-12T18:03:30Z)
• Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs [58.620269228776294]
  We propose a task-agnostic framework for resolving ambiguity by asking users clarifying questions. We evaluate systems across three NLP applications: question answering, machine translation and natural language inference. We find that intent-sim is robust, demonstrating improvements across a wide range of NLP tasks and LMs.
  arXiv  Detail & Related papers  (2023-11-16T00:18:50Z)
• 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)
• Quantifying Uncertainty in Natural Language Explanations of Large Language Models [29.34960984639281]
  Large Language Models (LLMs) are increasingly used as powerful tools for high-stakes natural language processing (NLP) applications. We propose two novel metrics -- $textitVerbalized Uncertainty$ and $textitProbing Uncertainty$ -- to quantify the uncertainty of generated explanations. Our empirical analysis of benchmark datasets reveals that verbalized uncertainty is not a reliable estimate of explanation confidence.
  arXiv  Detail & Related papers  (2023-11-06T21:14:40Z)

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.