Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

• URL: http://arxiv.org/abs/2409.16909v2
• Date: Sun, 29 Sep 2024 13:17:28 GMT
• Title: Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
• Authors: Wanqi Yang, Yanda Li, Meng Fang, Ling Chen,
• Abstract summary: Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts. We propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning.
• Score: 23.98067169669452
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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