MultiHaluDet: 通过LLM隐藏状态探测实现多语言幻觉检测
MultiHaluDet: Multilingual Hallucination Detection via LLM Hidden State Probing
AI总结：
提出MultiHaluDet框架，通过探测冻结LLM的全隐藏状态轨迹，结合多尺度注意力和自注意力池化的混合架构，以及校准的经典分类器集成，实现跨语言的高精度幻觉检测，在英语基准上达到98.55% AUROC，并展现出对高、中、低资源语言的强泛化能力。
摘要：
大型语言模型（LLM）中的幻觉是其可靠部署的关键障碍，这一漏洞在非英语和资源受限的环境中尤为严重。现有的依赖输出置信度启发式或单层内部表示的检测方法，往往无法捕捉跨语言的深层、复杂事实不一致性。为此，我们引入了MultiHaluDet，一种新颖的三阶段堆叠框架，通过探测冻结LLM的全隐藏状态轨迹来检测多语言幻觉，无需特定语言的微调。我们的方法提取跨多个层的序列特征，并通过使用多尺度注意力和自注意力池化的混合架构进行处理。通过生成折叠外嵌入并输入到校准的经典分类器集成中，MultiHaluDet捕捉了事实不一致性的细粒度和粗粒度模式。大量实验表明，我们的框架在Mistral-7B和LLaMA2-7B架构上，在英语HaluEval和TriviaQA基准测试中达到了高达98.55% AUROC的最先进检测性能。关键的是，我们严格评估了框架在高资源（法语）、中资源（孟加拉语）和低资源（阿姆哈拉语）语言上的跨语言泛化能力。MultiHaluDet展现出卓越的表示鲁棒性，始终优于基线，并成功地将幻觉检测能力迁移到类型多样的语言层级中。
英文摘要：
Hallucinations in Large Language Models (LLMs) represent a critical barrier to their reliable deployment, a vulnerability heavily exacerbated in non-English and resource-constrained contexts. Existing detection approaches that rely on output confidence heuristics or single-layer internal representations frequently fail to capture deep, complex factual inconsistencies across diverse languages. To address this, we introduce MultiHaluDet, a novel three-stage stacking framework that detects multilingual hallucinations by probing the full hidden state trajectories of frozen LLMs without requiring language-specific fine-tuning. Our method extracts sequential features across multiple layers and processes them via a hybrid architecture using multi-scale attention and self-attention pooling. By generating out-of-fold embeddings that feed into a calibrated classical classifier ensemble, MultiHaluDet captures both fine-grained and coarse-grained patterns of factual inconsistency. Extensive experiments demonstrate that our framework achieves state-of-the-art detection performance, reaching up to 98.55% AUROC on the English HaluEval and TriviaQA benchmarks using Mistral-7B and LLaMA2-7B architectures. Crucially, we rigorously evaluate our framework's cross-lingual generalization across high (French), medium (Bangla), and low-resource (Amharic) languages. MultiHaluDet demonstrates exceptional representational robustness, consistently outperforming baselines and successfully transferring hallucination detection capabilities across typologically diverse linguistic tiers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
