  Temporal reasoning over tabular data presents substantial challenges for
large language models (LLMs), as evidenced by recent research. In this study,
we conduct a comprehensive analysis of temporal datasets to pinpoint the
specific limitations of LLMs. Our investigation leads to enhancements in
TempTabQA, a dataset specifically designed for tabular temporal question
answering. We provide critical insights for improving LLM performance in
temporal reasoning tasks with tabular data. Furthermore, we introduce a novel
approach, C.L.E.A.R to strengthen LLM capabilities in this domain. Our findings
demonstrate that our method significantly improves evidence-based reasoning
across various models. Additionally, our experimental results reveal that
indirect supervision with auxiliary data substantially boosts model performance
in these tasks. This work contributes to a deeper understanding of LLMs'
temporal reasoning abilities over tabular data and promotes advancements in
their application across diverse fields.
