arXiv cs.CL· Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, Alexander Pashevich·· 3 天前AI 评分24
LLM 能否调用世界知识进行事件预测？DescribeEvent 基准给出答案
Can LLMs Access their World Knowledge for Event Prediction?
AI 导读
研究提出 DescribeEvent 基准，包含七个数据集、六个领域约 15 万条事件及事件级描述，用于检验 LLM 的事件预测能力。结果显示，能力较强的 LLM 在结合自然语言描述与符号标签后显著优于无描述变体及非 LLM 基线，而较弱模型收益有限。作者认为，LLM 的世界知识能否用于事件预测是接口设计问题，事件描述可有效激发模型已编码的知识。
正文 · 原文
Abstract:Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models. We ask whether this knowledge is truly inaccessible, or whether current methods fail to present event prediction in a form LLMs can use. We argue for the latter and show that LLMs become effective event forecasters when the interface is designed around two choices. The first grounds each event in a natural-language description, and the second separates event semantics from the prediction space to prevent semantic bias from label associations. To test this, we introduce DescribeEvent, a benchmark of ~150K events across seven datasets and six domains with event-level descriptions. We find that capable LLMs substantially benefit from descriptions and symbolic labels, outperforming description-free variants and non-LLM baselines, while weaker models gain little. These results suggest that accessing LLM world knowledge for event prediction is an interface problem, and that event descriptions offer a practical way to elicit the knowledge LLMs already encode.
| Comments: | Accepted to Findings of EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19021 [cs.CL] |
| (or arXiv:2610.19021v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19021 arXiv-issued DOI via DataCite |
Submission history
From: Zijun Wu [view email]
[v1]
Tue, 13 Oct 2026 18:14:05 UTC (281 KB)
来源：arXiv cs.CL · arxiv.org