摘要：时间和事件表达抽取是基础性的时间推理任务，但由于标注歧义性、领域敏感性以及模型行为的不稳定性，该任务依然极具挑战。现有的评估主要聚焦于领域内（in-domain）性能，对模型在分布偏移下的可靠性洞察有限。我们跨越模型家族、架构以及推理策略，评估了多种模型配置在四个泛化维度上的表现，考察了基础性能的迁移、跨维度相关性，以及模型规模、架构和提示（prompting）方法的影响。这为提示驱动的 LLM 在时间与事件表达抽取任务中的泛化表现提供了系统性研究。我们发现，较强的基础任务性能通常预示着更优的泛化能力。然而，在出现大幅度分布偏移时，这种关联会被削弱。归纳提示（Inductive prompting）在跨领域偏移、对抗扰动、组合性以及文本长度增加等维度上的表现最为稳健；而来自模型规模、架构以及演绎（deductive）与溯因（abductive）提示策略带来的增益则参差不齐且具有维度特异性。我们的结论是：LLM 在时间抽取任务中的泛化能力无法仅凭单一维度进行预测，也无法从领域内或单维度的评估中得到可靠推断，这凸显了探索能够跨维度泛化的推理策略的必要性。

Abstract:Time and event expression extraction are fundamental temporal reasoning tasks, but the problem remains difficult due to annotation ambiguity, domain sensitivity, and unstable model behavior. Existing evaluations focus on in-domain performance, offering limited insight into reliability under distribution shifts. We evaluate multiple model configurations across families, architectures, and reasoning strategies over four dimensions of generalization, examining transfer from base performance, cross-dimensional correlations, and the effects of scale, architecture, and prompting. This provides a systematic study of how prompted LLMs generalize in time and event expression extraction tasks. We find that strong base-task performance generally predicts better generalization. However, this relationship weakens under substantial distribution shifts. Inductive prompting performs most consistently across domain shift, adversarial perturbations, compositionality, and length increase, while gains from scale, architecture, and deductive and abductive prompting strategies are uneven and dimension-specific. We conclude that LLM generalization in temporal extraction tasks cannot be predicted from any single dimension alone and cannot be reliably inferred from in-domain or single-dimension evaluations, highlighting the need for reasoning strategies that generalize across dimensions.