提升大模型对时间信息的敏感度与推理能力，更好回答时间相关问题。
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
- 通过时间感知嵌入和细粒度对比强化学习增强时间理解
- 在四个数据集上显著优于现有大模型
- 适合需要精准时间推理的应用场景
时间敏感问答（TSQA）要求有效利用包含多个随时间演变的事实的时间上下文，以回答时间敏感问题。这不仅需要解析问题中的时间信息，还需识别和理解随时间变化的事实以生成准确答案。然而，当前大型语言模型对时间信息的敏感度仍有限，其时间推理能力不足。本文提出一种新框架，通过时间信息感知嵌入和细粒度对比强化学习增强时间意识与推理能力。在四个TSQA数据集上的实验结果表明，该框架在TSQA任务中显著优于现有大模型，标志着机器与人类在时间理解和推理能力之间的差距进一步缩小。
原文摘要 · Abstract (English)
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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