A Comprehensive Study of Knowledge Editing for Large Language Models

arXiv:2401.01286v4 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to …

abstract arxiv capabilities challenge communication computational cs.ai cs.cl cs.cv cs.hc cs.lg dynamic editing however human knowledge language language models large language large language models lies llms nature study text training type understanding