时间到不一致：大型语言模型对对抗攻击鲁棒性的生存分析
Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
AI总结：
本研究通过生存分析方法评估大型语言模型在多轮对话中的鲁棒性，发现语义漂移对不一致风险的影响，并提出轻量级模型用于提前检测失败对话。
摘要：
大型语言模型（LLMs）已彻底改变了对话式人工智能，但其在扩展的多轮对话中的鲁棒性仍缺乏深入理解。现有的评估框架专注于静态基准和单轮评估，未能捕捉到对话退化的时间动态，这在现实世界中是显著的。在本工作中，我们提出了一个大规模的对话鲁棒性生存分析，通过36,951次对话轮次，对9种最先进的LLM在MT-Consistency基准上的表现进行建模。我们的框架结合了Cox比例风险、加速失败时间（AFT）和随机生存森林模型，以及简单的语义漂移特征。我们发现，突然的提示到提示语义漂移显著增加了不一致的危险，而累积漂移出人意料地具有保护作用，表明在经历多次变化后能够适应的对话。具有模型漂移交互的AFT模型在区分和校准方面取得了最佳平衡，比例风险检查揭示了关键漂移协变量的系统性违反，解释了在该设置中Cox式建模的局限性。最后，我们展示了一个轻量级的AFT模型可以转换为一个轮级风险监控器，能够在第一次不一致回答之前几轮就标记大多数失败的对话，同时保持误报率较低。这些结果确立了生存分析作为评估多轮鲁棒性和设计实用保护措施的有力范式。
英文摘要：
Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present a large-scale survival analysis of conversational robustness, modeling failure as a time-to-event process over 36,951 turns from 9 state-of-the-art LLMs on the MT-Consistency benchmark. Our framework combines Cox proportional hazards, Accelerated Failure Time (AFT), and Random Survival Forest models with simple semantic drift features. We find that abrupt prompt-to-prompt semantic drift sharply increases the hazard of inconsistency, whereas cumulative drift is counterintuitively \emph{protective}, suggesting adaptation in conversations that survive multiple shifts. AFT models with model-drift interactions achieve the best combination of discrimination and calibration, and proportional hazards checks reveal systematic violations for key drift covariates, explaining the limitations of Cox-style modeling in this setting. Finally, we show that a lightweight AFT model can be turned into a turn-level risk monitor that flags most failing conversations several turns before the first inconsistent answer while keeping false alerts modest. These results establish survival analysis as a powerful paradigm for evaluating multi-turn robustness and for designing practical safeguards for conversational AI systems.
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。
