Multi-hop Question Answering under Temporal Knowledge Editing

• URL: http://arxiv.org/abs/2404.00492v1
• Date: Sat, 30 Mar 2024 23:22:51 GMT
• Title: Multi-hop Question Answering under Temporal Knowledge Editing
• Authors: Keyuan Cheng, Gang Lin, Haoyang Fei, Yuxuan zhai, Lu Yu, Muhammad Asif Ali, Lijie Hu, Di Wang,
• Abstract summary: Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models. Existing models for MQA under KE exhibit poor performance when dealing with questions containing explicit temporal contexts. We propose TEMPoral knowLEdge augmented Multi-hop Question Answering (TEMPLE-MQA) to address this limitation.
• Score: 9.356343796845662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models. However, existing models for MQA under KE exhibit poor performance when dealing with questions containing explicit temporal contexts. To address this limitation, we propose a novel framework, namely TEMPoral knowLEdge augmented Multi-hop Question Answering (TEMPLE-MQA). Unlike previous methods, TEMPLE-MQA first constructs a time-aware graph (TAG) to store edit knowledge in a structured manner. Then, through our proposed inference path, structural retrieval, and joint reasoning stages, TEMPLE-MQA effectively discerns temporal contexts within the question query. Experiments on benchmark datasets demonstrate that TEMPLE-MQA significantly outperforms baseline models. Additionally, we contribute a new dataset, namely TKEMQA, which serves as the inaugural benchmark tailored specifically for MQA with temporal scopes.

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