Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering

Autor:	Yang, Wanqi, Li, Yanda, Fang, Meng, Chen, Ling
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by EMNLP 2024 Findings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.16909 Zobrazit plný text záznamu View this record from Arxiv