Interruptive Prompting as Diagnostic Stress Testing of Local Large Language Models

This dataset accompanies the paper Interruptive Prompting as Diagnostic Stress Testing of Local Large Language Models and contains both the finalized manuscript and the raw interaction log used for analysis. The work documents a live, interrupt-driven Ollama session involving multiple locally hosted large language models (LLaMA-2 7B Chat, Phi-3 Mini, and Gemma 7B). By deliberately interrupting generation cycles and rapidly shifting prompt domains, the session functions as a lightweight behavioral stress test that reveals model-specific priors, alignment reflexes, verbosity biases, institutional mimicry, and refusal boundary geometry. Rather than evaluating benchmark accuracy or task performance, the study focuses on behavior under interruption, treating instability as signal rather than noise. The included interaction log serves as an executable empirical artifact supporting qualitative analysis in the paper. This dataset is intended for researchers interested in AI alignment diagnostics, local LLM evaluation, interpretability via interaction dynamics, and experimental methods that leverage adversarial play as a probe of model behavior. Files Interruptive Prompting As Diagnostic Stress Testing Of Local Large Language Models.pdf(58.67 KB, MD5: 181702f2f6c434efa7c93fa9433e1351)— Final academic manuscript. llm-local-log.md(MD5: 89a33b00cd7dfc271dd662a5063ec00f)— Raw Ollama session log used as the primary observational artifact.本数据集配套于论文《中断式提示作为本地大语言模型的诊断压力测试》（Interruptive Prompting as Diagnostic Stress Testing of Local Large Language Models），包含用于分析的终稿手稿与原始交互日志。 本研究记录了一场实时、由中断驱动的Ollama会话，涉及多款本地部署的大语言模型（Large Language Model，LLM）：LLaMA-2 7B Chat、Phi-3 Mini与Gemma 7B。通过刻意打断生成周期并快速切换提示域，该会话可作为轻量化行为压力测试，能够揭示模型专属先验知识、对齐反射、冗余偏倚、机构化模仿行为与拒绝边界的几何特征。 本研究并未评估基准准确率或任务性能，而是聚焦于中断场景下的模型行为，将不稳定性视为有效信号而非噪声。附带的交互日志可作为可执行的经验性实证样本，支撑论文中的定性分析。 本数据集面向关注AI对齐诊断、本地LLM评估、基于交互动态的可解释性研究，以及以对抗性交互作为模型行为探测手段的实验方法的科研人员。 文件 1. 《Interruptive Prompting As Diagnostic Stress Testing Of Local Large Language Models》.pdf（58.67 KB，MD5: 181702f2f6c434efa7c93fa9433e1351）—— 终版学术手稿。 2. llm-local-log.md（MD5: 89a33b00cd7dfc271dd662a5063ec00f）—— 作为主要观测样本的原始Ollama会话日志。