ShED-HD：一种用于边缘设备轻量级幻觉检测的香农熵分布框架
ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices
AI总结：
针对大语言模型幻觉检测在边缘设备等资源受限场景下的效率与精度平衡难题，提出轻量级ShED-HD框架，通过带单头注意力的BiLSTM分类序列熵模式，在分布外场景性能优于同类高效方法，分布内场景性能相当。
摘要：
大语言模型（LLM）已在各类自然语言处理（NLP）任务中展现出出色能力，但它们容易产生幻觉——即听起来合理但事实错误的内容——这在高风险领域带来了严峻挑战。现有的幻觉检测方法要么需要承担多次推理的计算成本，要么采用单次推理方法以牺牲准确性为代价换取效率，这两种方案在边缘设备等资源受限环境中都不理想。我们提出了香农熵分布幻觉检测器（Shannon Entropy Distribution Hallucination Detector，ShED-HD），这是一种新颖的幻觉检测框架，通过使用带单头注意力的轻量级双向长短期记忆网络（BiLSTM）架构对序列级熵模式进行分类，填补了这一空白。与现有方法不同，ShED-HD 能高效检测整个输出序列中独特的不确定性模式，同时保留上下文感知能力。通过在三个数据集（BioASQ、TriviaQA 和 Jeopardy Questions）上的深入评估，我们表明 ShED-HD 在分布外设置下显著优于其他计算高效的方法，同时在分布内设置下取得了相当的性能。ShED-HD 实现了低成本、准确且可泛化的幻觉检测，在可信AI功能至关重要的资源受限环境中，提升了LLM生成内容的可信度。
英文摘要：
Large Language Models (LLMs) have demonstrated impressive capabilities on a broad array of NLP tasks, but their tendency to produce hallucinations$\unicode{x2013}$plausible-sounding but factually incorrect content$\unicode{x2013}$poses severe challenges in high-stakes domains. Existing hallucination detection methods either bear the computational cost of multiple inference passes or sacrifice accuracy for efficiency with single-pass approaches, neither of which is ideal in resource-constrained environments such as edge devices. We propose the Shannon Entropy Distribution Hallucination Detector (ShED-HD), a novel hallucination detection framework that bridges this gap by classifying sequence-level entropy patterns using a lightweight BiLSTM architecture with single-headed attention. In contrast to prior approaches, ShED-HD efficiently detects distinctive uncertainty patterns across entire output sequences, preserving contextual awareness. Through in-depth evaluation on three datasets (BioASQ, TriviaQA, and Jeopardy Questions), we show that ShED-HD significantly outperforms other computationally efficient approaches in the out-of-distribution setting, while achieving comparable performance in the in-distribution setting. ShED-HD facilitates hallucination detection that is low-cost, accurate, and generalizable, improving the credibility of content generated by LLMs in resource-constrained environments where trustworthy AI functionality is crucial.
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。
