  The rapid advancements in large language models (LLMs) have ignited interest
in the temporal knowledge graph (tKG) domain, where conventional
embedding-based and rule-based methods 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 named GenTKG combining a temporal logical rule-based retrieval
strategy and few-shot parameter-efficient instruction tuning to solve the above
challenges, respectively. Extensive experiments have shown that GenTKG
outperforms conventional methods of temporal relational forecasting with low
computation resources using extremely limited training data as few as 16
samples. GenTKG also highlights remarkable cross-domain generalizability with
outperforming performance on unseen datasets without re-training, and in-domain
generalizability regardless of time split in the same dataset. Our work reveals
the huge potential of LLMs in the tKG domain and opens a new frontier for
generative forecasting on tKGs. Code and data are released here:
https://github.com/mayhugotong/GenTKG.
