Prompt-based uncertainty decomposition boosts LLM agent clarification-seeking
Gregory Matsnev proposes a prompt-based method that separates action confidence from request uncertainty in LLM agents, enabling proactive clarification-seeking on ambiguous tasks and outperforming baselines by up to 73% on new clarification benchmarks.
Score breakdown
The decomposition replaces impractical logprob- and training-based uncertainty methods with a prompt-only approach that works under real deployment constraints, enabling LLM agents to proactively seek clarification on ambiguous tasks rather than acting on underspecified instructions.
- 01The paper proposes a prompt-based decomposition separating action confidence from request uncertainty (`u`) to enable clarification-seeking in LLM agents.
- 02Two new benchmarks are introduced: WebShop-Clarification and ALFWorld-Clarification, each with 50% of tasks deliberately underspecified.
- 03The method is evaluated across five LLM backbones: GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, and GPT-OSS-120B.
Gregory Matsnev's paper responds to recent calls in the research community for uncertainty representations in LLM agents that go beyond the classical aleatoric/epistemic framework. The argument is that interactive agents need underspecification-aware, decomposed, and communicable uncertainty signals to support capabilities like 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 estimation methods, leaving prompt-based estimation as the most viable approach.
The method leads clarification F1 on every backbone on WebShop-Clarification and on four of five backbones on ALFWorld-Clarification.
The proposed solution is a simple prompt-based decomposition that separates action confidence from request uncertainty (`u`), enabling an agent to recognize when a task specification is ambiguous and ask for clarification rather than proceeding blindly. To benchmark this, the paper introduces WebShop-Clarification and ALFWorld-Clarification, two clarification-augmented variants of existing benchmarks in which 50% of tasks are deliberately underspecified. The method is compared against two baselines — ReAct+UE and Uncertainty-Aware Memory (UAM) — across five LLM backbones: GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, and GPT-OSS-120B.
Results show that, averaged across all five backbones, the decomposition improves clarification F1 on ALFWorld-Clarification by 73% over ReAct+UE and by 36% over UAM. The method leads clarification F1 on every backbone on WebShop-Clarification and on four of five backbones on ALFWorld-Clarification. The paper also evaluates on standard WebShop, ALFWorld, and REAL benchmarks for fault detection, suggesting the gains are not limited to clarification tasks alone.
Key facts
- 01The paper proposes a prompt-based decomposition separating action confidence from request uncertainty (`u`) to enable clarification-seeking in LLM agents.
- 02Two new benchmarks are introduced: WebShop-Clarification and ALFWorld-Clarification, each with 50% of tasks deliberately underspecified.
- 03The method is evaluated across five LLM backbones: GPT-5.1, DeepSeek-v3.2-exp, GLM-4.7, Qwen3.5-35B, and GPT-OSS-120B.
- 04Averaged across five backbones, clarification F1 on ALFWorld-Clarification improves by 73% over ReAct+UE and by 36% over UAM.
- 05The decomposition leads clarification F1 on every backbone on WebShop-Clarification and on four of five backbones on ALFWorld-Clarification.
- 06Logprob-based, multi-sampling, and training-based uncertainty methods are ruled out for deployment due to black-box APIs, latency budgets, and absent labeled trajectories.
- 07The method is also evaluated on standard WebShop, ALFWorld, and REAL benchmarks for fault detection.
Topics
Summary and scoring are generated automatically from the original article. We always link back to the publisher and never republish images or paywalled content. Last processed Jun 19, 2026 · 10:25 UTC. How this works →