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

2025-11-08

AI Summary

Hallucination detection in large language models (LLMs) faces two key challenges: rigid verification strategies that fail to adapt to dynamic execution contexts, and prohibitively high computational costs when relying on proprietary LLMs (e.g., GPT-4) for validation. To address these, we propose LEAP—a novel framework that formulates hallucination detection as a dynamic policy learning problem, enabling lightweight open-source student models to autonomously adapt their verification strategies. LEAP integrates a teacher-student architecture, dynamic learning loops, trajectory generation, policy distillation, and active correction mechanisms, achieving robust capability transfer via proxy-based fine-tuning. Evaluated on three challenging benchmarks, LEAP consistently outperforms existing state-of-the-art methods, delivering substantial gains in both detection accuracy and strategy adaptability while maintaining low inference overhead.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Hallucination in large language models (LLMs) remains a critical barrier to their safe deployment. Existing tool-augmented hallucination detection methods require pre-defined fixed verification strategies, which are crucial to the quality and effectiveness of tool calls. Some methods directly employ powerful closed-source LLMs such as GPT-4 as detectors, which are effective but too costly. To mitigate the cost issue, some methods adopt the teacher-student architecture and finetune open-source small models as detectors via agent tuning. However, these methods are limited by fixed strategies. When faced with a dynamically changing execution environment, they may lack adaptability and inappropriately call tools, ultimately leading to detection failure. To address the problem of insufficient strategy adaptability, we propose the innovative ``Learning to Evaluate and Adaptively Plan''(LEAP) framework, which endows an efficient student model with the dynamic learning and proactive correction capabilities of the teacher model. Specifically, our method formulates the hallucination detection problem as a dynamic strategy learning problem. We first employ a teacher model to generate trajectories within the dynamic learning loop and dynamically adjust the strategy based on execution failures. We then distill this dynamic planning capability into an efficient student model via agent tuning. Finally, during strategy execution, the student model adopts a proactive correction mechanism, enabling it to propose, review, and optimize its own verification strategies before execution. We demonstrate through experiments on three challenging benchmarks that our LEAP-tuned model outperforms existing state-of-the-art methods.

Problem

• Detecting hallucinations in large language models using adaptable verification strategies
• Reducing costs by distilling dynamic planning into efficient small models
• Enhancing strategy adaptability through proactive correction mechanisms

Innovation

• Dynamic learning loop for adaptive strategy adjustment
• Knowledge distillation from teacher to student model
• Proactive correction mechanism for strategy optimization