Uncertainty-Aware Clarification in LLM Agents with Information Gain
Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions. To address this challenge, we propose a goal-oriented clarification framework that aligns clarification behavior with ambiguity resolution. Central to our approach is the Information Gain Reward, a metric that quantifies the utility of clarification questions by measuring the Bayesian belief update towards the ground-truth goal induced by the clarification exchange. We train the clarifier (LLM) using this reward to optimize for high information gain, ensuring that clarifications effectively reduce uncertainty and improve task co...
| Authors | Mengyi Deng, Zhiwei Li, Xin Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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