We introduce CHEW, a novel dataset of changing events in Wikipedia expressed in naturally occurring text. We use CHEW for probing LLMs for their timeline understanding of Wikipedia entities and events in generative and classification experiments. Our results suggest that LLMs, despite having temporal information available, struggle to construct accurate timelines. We further show the usefulness of CHEW-derived embeddings for identifying meaning shift.

翻译：我们提出了CHEW，一个新颖的维基百科事件变迁数据集，其内容以自然发生的文本形式呈现。我们利用CHEW在生成与分类实验中探究大型语言模型对维基百科实体与事件的时间线理解能力。研究结果表明，尽管大型语言模型能够获取时间信息，但在构建准确时间线方面仍存在困难。我们进一步证明了基于CHEW生成的嵌入表示在识别语义变迁方面的实用价值。