Large language models (LLMs) learn temporal concepts from the co-occurrence of related tokens in a sequence. Compared with conventional text generation, temporal reasoning, which reaches a conclusion based on mathematical, logical and commonsense knowledge, is more challenging. In this paper, we propose TempGraph-LLM, a new paradigm towards text-based temporal reasoning. To be specific, we first teach LLMs to translate the context into a temporal graph. A synthetic dataset, which is fully controllable and requires minimal supervision, is constructed for pre-training on this task. We prove in experiments that LLMs benefit from the pre-training on other tasks. On top of that, we guide LLMs to perform symbolic reasoning with the strategies of Chain of Thoughts (CoTs) bootstrapping and special data augmentation. We observe that CoTs with symbolic reasoning bring more consistent and reliable results than those using free text.

我们提出了一种新的基于文本的时间推理模型TempGraph-LLM，通过将上下文翻译成时间图，教导大型语言模型 (LLMs) 学习时间概念。我们证明了在其他任务上的预训练对LLMs的效益，并通过思路链的引导和特殊数据增强引导LLMs进行符号推理，观察到符号推理带来更一致可靠的结果。

大型语言模型能够学习时间推理