Enhancing Temporal Understanding in LLMs for Semi-structured Tables
This work addresses temporal reasoning limitations in LLMs for semi-structured tables, which is an incremental improvement for applications requiring tabular data analysis.
The paper tackles the challenge of temporal reasoning over tabular data in large language models (LLMs) by analyzing temporal datasets and introducing the C.L.E.A.R method, which significantly improves evidence-based reasoning across various models and shows that indirect supervision with auxiliary data substantially boosts performance.
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.