  Large language models (LLMs) have made significant progress in natural
language processing (NLP), and are utilized extensively in various
applications. Recent works, such as chain-of-thought (CoT), have shown that
intermediate reasoning steps can improve the performance of LLMs for complex
reasoning tasks, such as math problems and symbolic question-answering tasks.
However, we notice the challenge that LLMs face when it comes to temporal
reasoning. Our preliminary experiments show that generating intermediate
reasoning steps does not always boost the performance of complex temporal
question-answering tasks. Therefore, we propose a novel framework that combines
the extraction capability of LLMs and the logical reasoning capability of a
Python solver to tackle this issue. Extensive experiments and analysis
demonstrate the effectiveness of our framework in handling intricate time-bound
reasoning tasks.
