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 paly-and-plug module to enhance the performance of graph-based models for TKG prediction. Extensive experiments on three datasets and backbones demonstrate the effectiveness of CoH.

翻译：时间知识图谱预测旨在基于给定的历史事实预测未来事件。当前多数基于图的模型擅长捕捉时间知识图谱中的结构信息，但缺乏语义理解能力。随着大型语言模型的兴起，基于大型语言模型的时间知识图谱预测模型应运而生。然而，现有基于大型语言模型的模型存在三个不足：(1) 仅关注一阶历史进行预测，忽略高阶历史信息，导致为大型语言模型提供的信息极为有限；(2) 大型语言模型在大量历史信息负荷下难以达到最优推理性能；(3) 针对时间知识图谱预测，大型语言模型自身的时间推理能力有限。为解决前两个挑战，我们提出历史链推理方法，逐步探索高阶历史信息，实现大型语言模型在时间知识图谱预测中对高阶历史信息的有效利用。针对第三个问题，我们将历史链设计为即插即用模块，以增强基于图的模型在时间知识图谱预测中的性能。在三个数据集和主干网络上进行的大量实验证明了历史链的有效性。