  Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based
on given histories. Most recent graph-based models excel at capturing
structural information within TKGs but lack semantic comprehension abilities.
Nowadays, with the surge of LLMs, the LLM-based TKG prediction model has
emerged. However, the existing LLM-based model exhibits three shortcomings: (1)
It only focuses on the first-order history for prediction while ignoring
high-order historical information, resulting in the provided information for
LLMs being extremely limited. (2) LLMs struggle with optimal reasoning
performance under heavy historical information loads. (3) For TKG prediction,
the temporal reasoning capability of LLM alone is limited. To address the first
two challenges, we propose Chain-of-History (CoH) reasoning which explores
high-order histories step-by-step, achieving effective utilization of
high-order historical information for LLMs on TKG prediction. To address the
third issue, we design CoH as a plug-and-play module to enhance the performance
of graph-based models for TKG prediction. Extensive experiments on three
datasets and backbones demonstrate the effectiveness of CoH.
