- Updated: August 17, 2026
- 1 min read
CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement
CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement
Authors: Kuangzhao Yang, Ziliang Zhao, Zhicheng Dou
Published: August 14, 2026
Abstract: In open-domain human‑computer interaction scenarios, large language models (LLMs) frequently encounter ambiguous or incomplete user queries. Direct answers can become over‑generalized, erroneous, or low‑information. Asking clarifying questions improves interaction quality, but existing methods rely heavily on manually annotated data or preference alignment. We propose CLAIM, an uncertainty‑driven framework for active clarification learning that eliminates the need for explicit human preference annotations by quantifying query uncertainty through entropy induced by answer disagreements across multiple models. This uncertainty signal drives synthetic data generation, enabling a unified clarification decision model trained via supervised learning and reinforcement learning.
The full paper is available on arXiv. For more insights and related resources, visit our internal pages: Clarification Framework, AI Research Hub, and Ubos Tech Blog.
Read the complete article below for an in‑depth technical discussion, methodology, experiments, and future directions.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.