[2511.05854] Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection

Computer Science > Artificial Intelligence arXiv:2511.05854 (cs) [Submitted on 8 Nov 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection Authors:Zepeng Bao, Shen Zhou, Qiankun Pi, Jianhao Chen, Mayi Xu, Ming Zhong, Yuanyuan Zhu, Tieyun Qian View a PDF of the paper titled Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection, by Zepeng Bao and 7 other authors View PDF HTML (experimental) Abstract:Hallucination in large language models (LLMs) remains a critical barrier to their safe deployment. For hallucination detection to be practical in real-world scenarios, the use of efficient small models is essential to ensure low latency and minimal resource consumption. However, existing methods rely on fixed verification strategies, where simply tuning small models to mimic fixed verification trajectories fails to capture the adaptability required for diverse hallucination patterns, thereby inducing planning instability. To address this limitation, we propose a ``Learning to Evaluate and Adaptively Plan'' (LEAP) framework, which shifts hallucination detection from fixed execution to dynamic strategy learning. Specifically, LEAP first employs a powerful teacher model to iteratively explore and refine verification strategies through a failure-driven loop. This dynamic planning capability is th...