Uncertainty Decomposition for Clarification Seeking in LLM Agents
Quick Answer
This paper introduces a prompt-based uncertainty decomposition for LLM agents, enhancing clarification seeking capabilities.
Quick Take
The proposed method improves clarification F1 scores by 73% over ReAct+UE and 36% over UAM across five , including GPT-5.1 and GLM-4.7, on new benchmarks designed for underspecified tasks.
Key Points
- Introduces a prompt-based method for decomposing uncertainty in LLM agents.
- Enhances proactive clarification seeking in ambiguous task specifications.
- Achieves a 73% improvement in F1 scores on ALFWorld-Clarification.
- Evaluated across five LLM backbones including GPT-5.1 and GLM-4.7.
- New benchmarks include WebShop-Clarification and ALFWorld-Clarification.
Paper Resources
Source Excerpt
Recent position papers argue that the classical aleatoric/epistemic uncertainty framework is insufficient for interactive (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 lo
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