The rapid advancements in large language models (LLMs) have ignited interest
in the temporal knowledge graph (tKG) domain, where conventional carefully
designed embedding-based and rule-based models dominate. The question remains
open of whether pre-trained LLMs can understand structured temporal relational
data and replace them as the foundation model for temporal relational
forecasting. Therefore, we bring temporal knowledge forecasting into the
generative setting. However, challenges occur in the huge chasms between
complex temporal graph data structure and sequential natural expressions LLMs
can handle, and between the enormous data sizes of tKGs and heavy computation
costs of finetuning LLMs. To address these challenges, we propose a novel
retrieval augmented generation framework that performs generative forecasting
on tKGs named GenTKG, which combines a temporal logical rule-based retrieval
strategy and lightweight parameter-efficient instruction tuning. Extensive
experiments have shown that GenTKG outperforms conventional methods of temporal
relational forecasting under low computation resources. GenTKG also highlights
remarkable transferability with exceeding performance on unseen datasets
without re-training. Our work reveals the huge potential of LLMs in the tKG
domain and opens a new frontier for generative forecasting on tKGs.

我们提出了一个新颖的检索增强生成框架 GenTKG，结合了时间逻辑规则检索策略和轻量级参数效率指导，用于在时间知识图上进行生成性预测，证实了大语言模型在时间知识图领域具有巨大潜力，并开辟了时间知识图生成性预测的新领域。