Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910624071548928 |
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| author | Yang, Wanqi Li, Yanda Fang, Meng Chen, Ling |
| author_facet | Yang, Wanqi Li, Yanda Fang, Meng Chen, Ling |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16909 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering Yang, Wanqi Li, Yanda Fang, Meng Chen, Ling Computation and Language Artificial Intelligence 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. |
| title | Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.16909 |