Enhancing Temporal Understanding in LLMs for Semi-structured Tables
<|reference_start|>Enhancing Temporal Understanding in LLMs for Semi-structured Tables: 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.<|reference_end|>
@article{deng2024enhancing,
    title={Enhancing Temporal Understanding in LLMs for Semi-structured Tables},
    author={Irwin Deng, Kushagra Dixit, Vivek Gupta, Dan Roth},
    journal={arXiv preprint arXiv:2407.16030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16030},
    primaryClass={cs.CL cs.AI cs.DB cs.LG}
}