1 code implementation • 25 Feb 2024 • Bozhong Tian, Siyuan Cheng, Xiaozhuan Liang, Ningyu Zhang, Yi Hu, Kouying Xue, Yanjie Gou, Xi Chen, Huajun Chen
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance.
no code implementations • 18 Feb 2024 • Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).
2 code implementations • 2 Jan 2024 • Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, Siyuan Cheng, Ziwen Xu, Xin Xu, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen
In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches.
Ranked #1 on knowledge editing on zsRE (using extra training data)
1 code implementation • 12 Oct 2023 • Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, Ningyu Zhang
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
2 code implementations • 14 Aug 2023 • Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
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cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
3 code implementations • 22 May 2023 • Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context.
1 code implementation • 18 Apr 2023 • Lei LI, Jing Chen, Bozhong Tian, Ningyu Zhang
Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP).
2 code implementations • 25 Jan 2023 • Siyuan Cheng, Ningyu Zhang, Bozhong Tian, Xi Chen, Qingbing Liu, Huajun Chen
To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.