  While large language models (LLMs) have demonstrated remarkable reasoning
capabilities, they are not without their flaws and inaccuracies. Recent studies
have introduced various methods to mitigate these limitations. Temporal
reasoning (TR), in particular, presents a significant challenge for LLMs due to
its reliance on diverse temporal concepts and intricate temporal logic. In this
paper, we propose TG-LLM, a novel framework towards language-based TR. Instead
of reasoning over the original context, we adopt a latent representation,
temporal graph (TG) that enhances the learning of TR. A synthetic dataset
(TGQA), which is fully controllable and requires minimal supervision, is
constructed for fine-tuning LLMs on this text-to-TG translation task. We
confirmed in experiments that the capability of TG translation learned on our
dataset can be transferred to other TR tasks and benchmarks. On top of that, we
teach LLM to perform deliberate reasoning over the TGs via Chain-of-Thought
(CoT) bootstrapping and graph data augmentation. We observed that those
strategies, which maintain a balance between usefulness and diversity, bring
more reliable CoTs and final results than the vanilla CoT distillation.
