Large language models (LLMs) often struggle with temporal reasoning, crucial
for tasks like historical event analysis and time-sensitive information
retrieval. Despite advancements, state-of-the-art models falter in handling
temporal information, especially when faced with irrelevant or noisy contexts.
This paper addresses this gap by empirically examining the robustness of
temporal question-answering (TQA) systems trained on various context types,
including relevant, irrelevant, slightly altered, and no context. Our findings
indicate that training with a mix of these contexts enhances model robustness
and accuracy. Additionally, we show that the position of context relative to
the question significantly impacts performance, with question-first positioning
yielding better results. We introduce two new context-rich TQA datasets,
ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines
for training robust TQA models. Our work lays the foundation for developing
reliable and context-aware temporal QA systems, with broader implications for
enhancing LLM robustness against diverse and potentially adversarial
information.

该论文通过经验性地考察训练在各种上下文类型中的时间问答（TQA）系统的鲁棒性，填补大型语言模型（LLM）在处理时态信息方面的不足，发现在涵盖相关、无关、稍微改动和无上下文的混合情况下训练，能增强模型的鲁棒性和准确性，并且问题先于上下文的位置布局有着明显影响。提出了两个新的上下文丰富的 TQA 数据集，ContextAQA 和 ContextTQE，并提供了全面的评估和指南，为培养鲁棒的 TQA 模型奠定了基础，对加强 LLM 在面对多样化和潜在对抗性信息时的鲁棒性具有广泛的影响。