NeurIPS 2025

Magical: Medical Lay Language Generation via
Semantic Invariance and Layperson-tailored
Adaptation
Weibin Liao♣♠†, Tianlong Wang♣†, Yinghao Zhu♢,
Yasha Wang♣♠∗, Junyi Gao♡§∗, Liantao Ma♣♠∗
♣National Engineering Research Center For Software Engineering, Peking University
♠School of Computer Science, Peking University
♢School of Computing and Data Science, The University of Hong Kong
♡Centre for Medical Informatics, University of Edinburgh
§Health Data Research UK
{liaoweibin, tianlong.wang}@stu.pku.edu.cn, malt@pku.edu.cn
https://github.com/tianlwang/Magical.git
Abstract
Medical Lay Language Generation (MLLG) plays a vital role in improving the
accessibility of complex scientific content for broader audiences. Recent literature
to MLLG commonly employ parameter-efficient fine-tuning methods such as Low-
Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using
paired expert-lay language datasets. However, LoRA struggles with the challenges
posed by multi-source heterogeneous MLLG datasets. Specifically, through a
series of exploratory experiments, we reveal that standard LoRA fail to meet the
requirement for semantic fidelity and diverse lay-style generation in MLLG task. To
address these limitations, we propose Magical, an asymmetric LoRA architecture
tailored for MLLG under heterogeneous data scenarios. Magical employs a shared
matrix A for abstractive summarization, along with multiple isolated matrices B for
diverse lay-style generation. To preserve semantic fidelity during the lay language
generation process, Magical introduces a Semantic Invariance Constraint
to mitigate semantic subspace shifts on matrix A. Furthermore, to better adapt to
diverse lay-style generation, Magical incorporates the Recommendation-guided
Switch, an externally interface to prompt the LLM to switch between different
matrices B. Experimental results on three real-world lay language generation
datasets demonstrate that Magical consistently outperforms prompt-based methods,...