Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
<|reference_start|>Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering: Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and understanding of time-evolving facts to generate accurate answers. However, current large language models still have limited sensitivity to temporal information and their inadequate temporal reasoning capabilities. In this paper, we propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning. Experimental results on four TSQA datasets demonstrate that our framework significantly outperforms existing LLMs in TSQA tasks, marking a step forward in bridging the performance gap between machine and human temporal understanding and reasoning.<|reference_end|>
@article{yang2024enhancing,
    title={Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question
  Answering},
    author={Wanqi Yang, Yanda Li, Meng Fang, Ling Chen},
    journal={arXiv preprint arXiv:2409.16909},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16909},
    primaryClass={cs.CL cs.AI}
}