🤖 AI Summary
This work addresses the challenge of hallucinations in large language models (LLMs), which hinder their reliable deployment in high-stakes applications. The authors propose a novel hallucination detection method that leverages gradient patterns across model layers during a single forward–backward pass. They discover, for the first time, that over 97% of discriminative gradient signals are concentrated in the final five layers of the model. Building upon this insight, they develop an efficient and interpretable unified framework capable of simultaneously identifying hallucinated content and predicting when the model should abstain from answering. Experimental results across multiple question-answering benchmarks demonstrate that the proposed approach significantly outperforms baseline methods based on confidence scores or sampling strategies, achieving high performance with minimal computational overhead.
📝 Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applications. We present Grad Detect, a gradient-based approach for predicting hallucinations by analyzing layer-wise gradient patterns from a single forward-backward pass during inference. Our method shows that the internal gradient structure of a model carries rich information about the correctness of its output. This information is not accessible through output-level signals alone. We evaluate Grad Detect on several Q&A benchmarks across both hallucination detection and model abstention prediction, where it consistently outperforms confidence-based and sampling-based baselines. Through comprehensive layer ablation studies across all eleven models from four architectural families, we find that the final five layers concentrate over 97% of the discriminative gradient signal, enabling efficient deployment with minimal performance loss. Grad Detect provides a unified framework for predicting multiple dimensions of LLM reliability, offering strong predictive performance alongside interpretable insights into where and how model failures originate.