Medical Text Simplification From Jargon Detection to Jargon-Aware Prompting (2025)

Abstract: Jargon identification is critical for improving the accessibility of biomedical texts yet models are often evaluated on isolated datasets leaving open questions about generalization. After reproducing MedReadMes jargon detection results and extending evaluation to the PLABA dataset we find that transfer learning across datasets yields only modest gains largely due to divergent annotation objectives. Through manual re-annotation we show that aligning labeling schemes improves cross-dataset performance. Building on these findings we evaluate several jargon-aware prompting strategies for LLM-based medical text simplification. Explicitly highlighting jargon in prompts does not consistently improve simplification quality. When gains occur they often trade off against readability and are model-dependent. Human evaluation indicates that simple prompting can be as effective as more complex jargon-aware instructions. We release code to facilitate further research https//anonymous.4open.science/r/tsar-anonymous-2D66F/README.md

## introduction medical text simplification is crucial for improving health literacy by making technical content accessible to lay readers, with jargon handling being a central challenge. in this work, we define jargon as any term or span of words that may be hard to understand for lay readers who are not in the medical domain, such as technical abbreviations or complex terminology requiring simplification. however, jargon detection models are often evaluated on isolated datasets, leaving significant questions about their generalization capabilities. recent resources like medreadme @xcite and plaba @xcite provide valuable benchmarks for jargon-centric research, but they employ different annotation schemes, medreadme categorizes term difficulty for lay readers, while plaba identifies ## related work text simplification aims to make specialized content accessible without sacrificing meaning, a particular challenge in medicine where technical terminology is dense @xcite . our work connects four areas: lexical complexity detection, biomedical simplification resources, llm prompting strategies, and evaluation. lexical complexity and jargon detection have evolved from surface heuristics to contextual models like bert @xcite , with medreadme providing fine-grained jargon categories for lay reader difficulty analysis @xcite . work on biomedical text simplification leverages datasets such as plaba, which offers expertauthored adaptations with span-level links to technical terms @xcite , though @xcite and others highlight challenges in sentence-level alignment @xcite @xcite . there has been limited exploration of llm prompting strategies that explicitly surface jargon to control simplification @xcite . for evaluation, we assess how automatic metrics like fkgl and sari @xcite align with human judgments in this jargon-aware setting. the plaba dataset has enabled research into more controlled simplification approaches. notably, @xcite conducted a study on jargonaware simplification by using detected jargon spans to structure prompts for large language models. their findings suggest that while explicitly surfacing jargon can be beneficial, its effectiveness is not consistent across models and often comes at the cost of readability, highlighting the complexity of integrating detection with generation. ## limitations the lack of multiple annotators for manual reannotation is a limitation and should be addressed in future work to improve reliability. the smallscale human evaluation (only 5 annotators from a computer science background, evaluating just 3 abstracts each) and resulting high rater disagreement may limit generalizability of perceived quality. automatic metrics like sari may not fully capture jargon-specific changes, and the low bleu scores in simplification experiments indicate challenges in generating high-quality outputs. while the evaluation was at the document level, generation was at the sentence level, losing the global context. future work could explore better prompt engineering, larger-scale evaluations with diverse annotators, and document-level generation to address these issues. ## lay summary medical texts are full of complex terms that can confuse people without a scientific background. this makes it hard for patients and the general public to understand health information. our research focuses on two key areas: identifying these difficult terms (called "jargon") and simplifying medical texts so they're easier to read. first, we studied how well computer models can spot jargon in medical writing. we compared two datasets: medreadme, which labels terms by how hard they are for lay people to understand, and plaba, which marks terms that experts think need simplifying. we found that models trained on one dataset don't work as well on the other because the datasets have different goals. but when we manually relabeled some plaba data to match medreadme's style, the models improved a lot, showing that aligning how we define jargon helps cross-dataset learning. second, we tested ways to make large language models (like ai chatbots) simplify medical texts. we tried simple prompts and more complex ones that highlight detected jargon. surprisingly, the simple prompts often worked just as well or better than the jargon-focused ones. results depended on the model-general-purpose models liked more guidance, but specialized medical models did better with basics. this suggests that just telling an ai to simplify might be enough, without needing to point out every jargon term. our work shows that making medical info ac-cessible is tricky, but better data alignment and smarter prompting can help. we hope this leads to tools that make health communication clearer for everyone, improving patient understanding and outcomes. all our code and data are publicly available to support future research.