系统幻觉量表（SHS）：一种最小但有效的以人类为中心的评估工具，用于评估大语言模型中的幻觉相关行为
The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models
AI总结：
SHS是一种以人类为中心的评估工具，用于评估大语言模型中的幻觉相关行为，通过用户视角提供快速且可解释的评估方法。
摘要：
我们介绍了系统幻觉量表（SHS），一种轻量级且以人类为中心的评估工具，用于评估大语言模型（LLMs）中的幻觉相关行为。受已建立的心理测量工具如系统可用性量表（SUS）和系统因果性量表（SCS）的启发，SHS能够快速、可解释、且领域无关地评估事实不可靠性、不连贯性、误导性呈现以及模型生成文本对用户指导的响应性。SHS明确不是自动幻觉检测器或基准度量指标；相反，它捕捉了在现实交互条件下，幻觉现象如何从用户视角表现出来。210名参与者的实际评估显示了高清晰度、连贯的响应行为和结构效度，支持通过统计分析包括内部一致性（Cronbach's alpha = 0.87）和显著的维度相关性（p < 0.001）来验证。与SUS和SCS的比较分析揭示了互补的测量特性，支持SHS作为比较分析、迭代系统开发和部署监控的实用工具。
英文摘要：
We introduce the System Hallucination Scale (SHS), a lightweight and human-centered measurement instrument for assessing hallucination-related behavior in large language models (LLMs). Inspired by established psychometric tools such as the System Usability Scale (SUS) and the System Causability Scale (SCS), SHS enables rapid, interpretable, and domain-agnostic evaluation of factual unreliability, incoherence, misleading presentation, and responsiveness to user guidance in model-generated text. SHS is explicitly not an automatic hallucination detector or benchmark metric; instead, it captures how hallucination phenomena manifest from a user perspective under realistic interaction conditions. A real-world evaluation with 210 participants demonstrates high clarity, coherent response behavior, and construct validity, supported by statistical analysis including internal consistency (Cronbach's alpha = 0.87$) and significant inter-dimension correlations (p < 0.001$). Comparative analysis with SUS and SCS reveals complementary measurement properties, supporting SHS as a practical tool for comparative analysis, iterative system development, and deployment monitoring.
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