March 29, 2024, 4:43 a.m. | Yucheng Shi, Qiaoyu Tan, Xuansheng Wu, Shaochen Zhong, Kaixiong Zhou, Ninghao Liu

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.19631v1 Announce Type: cross
Abstract: Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or inaccurate responses. This problem becomes even more challenging when dealing with multi-hop questions since they require LLMs to update and integrate multiple knowledge pieces relevant to the questions. To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework tailored for multi-hop question answering. RAE first retrieves edited facts and then …

abstract arxiv cs.ai cs.cl cs.lg editing knowledge language language models large language large language models llms multiple question question answering questions real-time responses retrieval struggle tasks type update updates

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