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.

翻译：时间敏感问答（TSQA）要求有效利用特定的时间上下文（包含多个随时间演变的事实）来回答时间敏感问题。这不仅需要解析问题中的时间信息，还需要识别和理解随时间演变的事实以生成准确答案。然而，当前的大型语言模型对时间信息的敏感性仍然有限，且其时间推理能力不足。本文提出了一种新颖的框架，通过时间信息感知嵌入与粒度对比强化学习来增强时间感知与推理能力。在四个TSQA数据集上的实验结果表明，我们的框架在TSQA任务中显著优于现有的大型语言模型，标志着在缩小机器与人类在时间理解与推理方面的性能差距上迈出了重要一步。