March 20, 2024, 4:43 a.m. | Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni

cs.LG updates on arXiv.org arxiv.org

arXiv:2308.07269v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, …

abstract arxiv cs.ai cs.cl cs.cv cs.ir cs.lg data easy editing events facts framework generate knowledge language language models large language large language models llms text type

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