Uncertainty-Aware Clarification in LLM Agents with Information Gain
The article discusses a new framework for Large Language Model (LLM) agents that aims to improve their performance in situations with unclear user instructions. This framework utilizes an Information Gain Reward to enhance the effectiveness of clarification questions, thereby reducing uncertainty and improving task completion. Empirical results indicate that the proposed method increases the success rate of LLM agents while minimally affecting interaction steps.
- ▪The framework addresses challenges posed by underspecified user instructions in LLM agents.
- ▪It employs an Information Gain Reward to optimize clarification questions.
- ▪The method has shown a 3.7% improvement in success rates over a no-clarification baseline.
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Computer Science > Artificial Intelligence arXiv:2606.03135 (cs) [Submitted on 2 Jun 2026] Title:Uncertainty-Aware Clarification in LLM Agents with Information Gain Authors:Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Ying Zhao, Zhijiang Guo, Wei Wang View a PDF of the paper titled Uncertainty-Aware Clarification in LLM Agents with Information Gain, by Mengyi Deng and 6 other authors View PDF HTML (experimental) Abstract: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.
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