  Model editing aims to precisely alter the behaviors of large language models
(LLMs) in relation to specific knowledge, while leaving unrelated knowledge
intact. This approach has proven effective in addressing issues of
hallucination and outdated information in LLMs. However, the potential of using
model editing to modify knowledge in the medical field remains largely
unexplored, even though resolving hallucination is a pressing need in this
area. Our observations indicate that current methods face significant
challenges in dealing with specialized and complex knowledge in medical domain.
Therefore, we propose MedLaSA, a novel Layer-wise Scalable Adapter strategy for
medical model editing. MedLaSA harnesses the strengths of both adding extra
parameters and locate-then-edit methods for medical model editing. We utilize
causal tracing to identify the association of knowledge in neurons across
different layers, and generate a corresponding scale set from the association
value for each piece of knowledge. Subsequently, we incorporate scalable
adapters into the dense layers of LLMs. These adapters are assigned scaling
values based on the corresponding specific knowledge, which allows for the
adjustment of the adapter's weight and rank. The more similar the content, the
more consistent the scale between them. This ensures precise editing of
semantically identical knowledge while avoiding impact on unrelated knowledge.
To evaluate the editing impact on the behaviours of LLMs, we propose two model
editing studies for medical domain: (1) editing factual knowledge for medical
specialization and (2) editing the explanatory ability for complex knowledge.
We build two novel medical benchmarking datasets and introduce a series of
challenging and comprehensive metrics. Extensive experiments on medical LLMs
demonstrate the editing efficiency of MedLaSA, without affecting unrelated
knowledge.
