1 citations · 1 across the 5 of their papers we have counts for
5 papers
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
Yunzhi Yao, Jizhan Fang, Jia-Chen Gu +4
Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they ofte…
Constraining Sequential Model Editing with Editing Anchor Compression
Hao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling +2
Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining these models, there is an increasing foc…
AnyEdit: Edit Any Knowledge Encoded in Language Models
Houcheng Jiang, Junfeng Fang, Ningyu Zhang +5
Large language models (LLMs) often produce incorrect or outdated information, necessitating efficient and precise knowledge updates. Current model editing methods, however, struggl…
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Ningyu Zhang, Bozhong Tian, Siyuan Cheng +6
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current a…
Neighboring Perturbations of Knowledge Editing on Large Language Models
Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang +1
Despite their exceptional capabilities, large language models (LLMs) are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature…