  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frequent updates to LLMs to correct outdated information or integrate new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 address
deficiencies or undesirable behaviors. There is an increasing interest in
efficient, lightweight methods for on-the-fly model modifications. To this end,
recent years have seen a burgeoning in the techniques of knowledge editing for
LLMs, which aim to efficiently modify LLMs' behaviors within specific domains
while preserving overall performance across various inputs. In this paper, we
first define the knowledge editing problem and then provide a comprehensive
review of cutting-edge approaches. Drawing inspiration from educational and
cognitive research theories, we propose a unified categorization criterion that
classifies knowledge editing methods into three groups: resorting to external
knowledge, merging knowledge into the model, and editing intrinsic knowledge.
Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive
empirical evaluation of representative knowledge editing approaches.
Additionally, we provide an in-depth analysis of knowledge location, which can
give a deeper understanding of the knowledge structures inherent within LLMs.
Finally, we discuss several potential applications of knowledge editing,
outlining its broad and impactful implications.
