Abstract
Large language models(LLMs) excel at text generation and knowledge question-answering tasks, but they are prone to generating hallucinated content, severely limiting their application in high-risk domains. Current hallucination detection methods based on uncertainty estimation and external knowledge retrieval suffer from the limitation that they still produce erroneous content at high confidence levels and rely heavily on retrieval efficiency and knowledge coverage. In contrast, probe methods that leverage the model's hidden-layer states offer real-time and lightweight advantages. However, traditional linear probes struggle to capture nonlinear structures in deep semantic this http URL overcome these limitations, we propose a neural network-based framework for token-level hallucination detection. By freezing language model parameters, we employ lightweight MLP probes to perform nonlinear modeling of high-level hidden states. A multi-objective joint loss function is designed to enhance detection stability and semantic disambiguity. Additionally, we establish a layer position-probe performance response model, using Bayesian optimization to automatically search for optimal probe insertion layers and achieve superior training this http URL results on LongFact, HealthBench, and TriviaQA demonstrate that MLP probes significantly outperform state-of-the-art methods in accuracy, recall, and detection capability under low false-positive conditions.
Abstract (translated)
大型语言模型（LLMs）在文本生成和知识问答任务中表现出色，但它们容易产生虚构内容，这严重限制了其在高风险领域的应用。当前基于不确定性估计和外部知识检索的幻觉检测方法存在局限性：即便自信度很高时仍会产生错误内容，并且高度依赖于检索效率和知识覆盖范围。相比之下，利用模型隐藏层状态的探针方法提供了实时性和轻量级的优势。然而，传统的线性探针难以捕捉深层语义中的非线性结构。为克服这些限制，我们提出了一种基于神经网络的框架来进行标记级别的幻觉检测。通过冻结语言模型参数，我们使用轻量级MLP探针对高级隐藏状态进行非线性建模。设计了一个多目标联合损失函数以增强检测稳定性和语义歧义消除能力。此外，建立了层位置-探针性能响应模型，并采用贝叶斯优化自动搜索最佳的探针插入层，从而实现优越的训练效果。在LongFact、HealthBench和TriviaQA上的实验结果表明，在低假阳性条件下，MLP探针对比当前最先进的方法在准确性、召回率和检测能力方面有显著提升。
URL
https://arxiv.org/abs/2512.20949
