Uncertainty Decomposition for Clarification Seeking in LLM Agents

Researchers introduce a prompt-based uncertainty decomposition method that enables LLM agents to proactively seek clarification when task specifications are ambiguous. The approach separates action confidence from request uncertainty and demonstrates 36-73% improvements in clarification performance across multiple LLM backbones compared to existing uncertainty frameworks.

This research addresses a fundamental challenge in deploying interactive LLM agents: the inability to recognize and respond to ambiguous task specifications. Traditional uncertainty quantification frameworks distinguish between aleatoric (data) and epistemic (model) uncertainty, but fail to capture underspecification—when tasks themselves lack sufficient information. The proposed solution is elegantly simple: a prompt-based decomposition that enables agents to distinguish between confidence in executing a known task versus uncertainty about what the task actually requires.

The work emerges from a growing recognition that LLM agents operating in real-world environments need communicable uncertainty representations that can support human-AI collaboration. Current deployment constraints—reliance on black-box APIs, latency requirements, and limited labeled data—eliminate computationally expensive alternatives like multi-sampling or training-based approaches. This makes prompt-based estimation the most practical path forward for production systems.

The evaluation methodology strengthens the contribution significantly. By introducing two clarification-augmented benchmarks where 50% of tasks are deliberately underspecified, the researchers create realistic testing conditions. Testing across five LLM backbones (including GPT-5.1, DeepSeek-v3.2-exp, and others) demonstrates generalization beyond single-model dependencies—a critical requirement for robust deployment. The 73% improvement on ALFWorld-Clarification over existing baselines suggests meaningful practical value.

For developers building interactive AI systems, this work provides an immediately deployable technique for improving agent reliability. The method's simplicity—achieved through prompting rather than architectural changes—makes adoption straightforward. As LLM-based applications move toward more autonomous and interactive deployments, the ability to seek clarification becomes increasingly important for avoiding costly mistakes and maintaining user trust.

• →Prompt-based uncertainty decomposition separates action confidence from task specification ambiguity, enabling proactive clarification seeking.
• →Method improves clarification F1 scores by 36-73% compared to ReAct+UE and UAM baselines across multiple LLM models.
• →Approach works with black-box APIs and within interactive latency constraints, making it immediately deployable in production systems.
• →Generalization across five different LLM backbones demonstrates robustness beyond single-model dependencies.
• →Research establishes clarification-augmented benchmarks that reflect real-world underspecification challenges in agent deployment.