Editing Factual Knowledge and Explanatory Ability of Medical Large
  Language Models
Derong Xu Ziheng Zhang Zhihong Zhu Zhenxi Lin Qidong Liu Xian Wu Tong Xu Xiangyu Zhao Yefeng Zheng Enhong Chen
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out-of-date issues in LLMs.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.