Model editing aims to precisely modify the behaviours of large language
models (LLMs) on specific knowledge while keeping irrelevant knowledge
unchanged. It has been proven effective in resolving hallucination and
out-of-date issues in LLMs. As a result, it can boost the application of LLMs
in many critical domains (e.g., medical domain), where the hallucination is not
tolerable. In this paper, we propose two model editing studies and validate
them in the medical domain: (1) directly editing the factual medical knowledge
and (2) editing the explanations to facts. Meanwhile, we observed that current
model editing methods struggle with the specialization and complexity of
medical knowledge. Therefore, we propose MedLaSA, a novel Layer-wise Scalable
Adapter strategy for medical model editing. It employs causal tracing to
identify the precise location of knowledge in neurons and then introduces
scalable adapters into the dense layers of LLMs. These adapters are assigned
scaling values based on the corresponding specific knowledge. To evaluate the
editing impact, we build two benchmark datasets and introduce a series of
challenging and comprehensive metrics. Extensive experiments on medical LLMs
demonstrate the editing efficiency of MedLaSA, without affecting irrelevant
knowledge that is not edited.

提出了两项模型编辑研究并在医学领域进行了验证，同时观察到当前模型编辑方法在医学知识的专业化和复杂性方面存在困难。因此，提出了一种用于医学模型编辑的新型分层可扩展适配器策略 MedLaSA。通过因果追踪来确定神经元中知识的准确位置，并在 LLMs 的密集层引入可扩展适配器，这些适配器基于相应的特定知识分配缩放值。在医疗 LLMs 上进行的大量实验证明了 MedLaSA 的编辑效率，且不影响未编辑的无关知识。