2026-06-19原文2 min readUpdated: 2026-06-19

Uncertainty Decomposition for Clarification Seeking in LLM Agents

This paper proposes a prompt-based uncertainty decomposition method that separates action confidence from request uncertainty, enabling LLM agents to ask for clarification when task specifications are ambiguous. The authors introduce two new benchmarks with 50% underspecified tasks and evaluate against existing methods across five LLMs, showing significant F1 improvements.

SourcearXiv AIAuthor: Gregory Matsnev

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Key points

Classical aleatoric/epistemic uncertainty frameworks are insufficient for interactive LLM agents; underspecification-aware representations are needed.
A simple prompt-based decomposition separates action confidence from request uncertainty, enabling proactive clarification.
Two new benchmarks (WebShop-Clarification and ALFWorld-Clarification) are introduced with 50% deliberately underspecified tasks.
The proposed method improves clarification F1 by 73% over ReAct+UE and 36% over UAM on average across five LLM backbones.

Why it matters

This matters because classical aleatoric/epistemic uncertainty frameworks are insufficient for interactive LLM agents; underspecification-aware representations are needed.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2606.19559] Uncertainty Decomposition for Clarification Seeking in LLM Agents

[Submitted on 17 Jun 2026]

Title:Uncertainty Decomposition for Clarification Seeking in LLM Agents

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Abstract:Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive large language model (LLM) agents and call for underspecification-aware, decomposed, and communicable uncertainty representations that can unlock new agent capabilities such as proactive clarification seeking and shared mental-model building. Practical deployment constraints -- black-box APIs, interactive latency budgets, and the absence of labeled trajectories -- rule out logprob-based, multi-sampling, and training-based methods, leaving prompt-based estimation as the most viable family for surfacing such signals at deployment time. We answer this call with a simple prompt-based decomposition that separates action confidence from request uncertainty (u), enabling the agent to ask for clarification when the task specification is ambiguous. To evaluate it, we introduce two clarification-augmented benchmarks (WebShop-Clarification and ALFWorld-Clarification) in which 50% of tasks are deliberately underspecified, and systematically compare the proposed decomposition against ReAct+UE and Uncertainty-Aware Memory (UAM) across five LLM backbones (GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, GPT-OSS-120B) on these variants together with the standard WebShop, ALFWorld, and REAL benchmarks for fault detection. Averaged across the five backbones, the proposed decomposition improves clarification F1 on ALFWorld-Clarification by 73% over ReAct+UE and by 36% over UAM, and leads clarification F1 on every backbone on WebShop-Clarification and on four of five backbones on ALFWorld-Clarification, indicating that the gains generalize beyond a single LLM.

Comments: 26 pages, 8 figures. Source code: this https URL

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as: arXiv:2606.19559 [cs.AI]

(or arXiv:2606.19559v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.19559

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Gregory Matsnev [view email] [v1] Wed, 17 Jun 2026 19:59:32 UTC (1,420 KB)

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