5 citations · 6 across the 11 of their papers we have counts for
4 papers · 2 filters
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
Qizhou Chen, Chengyu Wang, Dakan Wang +3
Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This ta…
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
Taolin Zhang, Dongyang Li, Qizhou Chen +5
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the halluci…
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
Taolin Zhang, Qizhou Chen, Dongyang Li +5
Recently, while large language models (LLMs) have demonstrated impressive results, they still suffer from hallucination, i.e., the generation of false information. Model editing is…
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Qizhou Chen, Taolin Zhang, Xiaofeng He +4
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging…