Uncertainty Decomposition for Clarification Seeking in LLM Agents

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new prompt-based uncertainty decomposition for large language model (LLM) agents has been developed to enhance clarification seeking in ambiguous tasks. This method specifically separates an agent's action confidence from its request uncertainty, enabling proactive clarification when task specifications are underspecified. To validate its effectiveness, researchers introduced two novel benchmarks, WebShop-Clarification and ALFWorld-Clarification, where half of the tasks are intentionally ambiguous. The decomposition was systematically evaluated against existing methods like ReAct+UE and Uncertainty-Aware Memory (UAM) across five diverse LLM backbones: GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, and GPT-OSS-120B. Averaged results show the proposed approach improved clarification F1 on ALFWorld-Clarification by 73% over ReAct+UE and 36% over UAM, demonstrating generalizable gains across most tested LLMs.

Key takeaway

For AI Engineers developing interactive LLM agents, you should consider integrating prompt-based uncertainty decomposition to enhance agent robustness. This method allows your agents to proactively seek clarification for ambiguous task specifications, improving performance on underspecified tasks. By separating action confidence from request uncertainty, you can achieve substantial gains in clarification F1, as demonstrated by improvements of up to 73% over existing methods on new clarification benchmarks.

Key insights

Prompt-based uncertainty decomposition enables LLM agents to proactively seek clarification for underspecified tasks.

Principles

• Aleatoric/epistemic framework is insufficient for interactive LLMs.
• Decomposed uncertainty improves agent capabilities.
• Prompt-based estimation is viable for black-box APIs.

Method

A simple prompt-based decomposition separates action confidence from request uncertainty (u), allowing agents to ask for clarification when task specifications are ambiguous.

In practice

• Implement prompt-based uncertainty for clarification.
• Use WebShop-Clarification for evaluation.
• Compare against ReAct+UE or UAM.

Topics

• Large Language Models
• Agent Systems
• Uncertainty Quantification
• Clarification Seeking
• Prompt Engineering
• Benchmarking

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.