Temporal knowledge graph (TKG) forecasting benchmarks challenge models to
predict future facts using knowledge of past facts. In this paper, we apply
large language models (LLMs) to these benchmarks using in-context learning
(ICL). We investigate whether and to what extent LLMs can be used for TKG
forecasting, especially without any fine-tuning or explicit modules for
capturing structural and temporal information. For our experiments, we present
a framework that converts relevant historical facts into prompts and generates
ranked predictions using token probabilities. Surprisingly, we observe that
LLMs, out-of-the-box, perform on par with state-of-the-art TKG models carefully
designed and trained for TKG forecasting. Our extensive evaluation presents
performances across several models and datasets with different characteristics,
compares alternative heuristics for preparing contextual information, and
contrasts to prominent TKG methods and simple frequency and recency baselines.
We also discover that using numerical indices instead of entity/relation names,
i.e., hiding semantic information, does not significantly affect the
performance ($\pm$0.4\% Hit@1). This shows that prior semantic knowledge is
unnecessary; instead, LLMs can leverage the existing patterns in the context to
achieve such performance. Our analysis also reveals that ICL enables LLMs to
learn irregular patterns from the historical context, going beyond simple
predictions based on common or recent information.

本文研究利用大型语言模型和上下文学习技术进行时间知识图谱（TKG）预测，并证明可在不需要显式重点捕获结构和时态信息的情况下，LLM 与针对 TKG 预测深度设计和训练的最先进的 TKG 模型表现相当，而且本文发现使用数值索引而非实体 / 关系名称可以实现接近相同的性能，且证明上下文学习可以帮助 LLMs 学习历史上不规律的模式，从而超越基于共同或最新信息的简单预测。