Year: 2026
Type: conference-paper
Abstract: The variation in writing style encapsulates nuanced characteristics, which are often exploited for author or demographic identification.In the medical domain, language models are frequently deployed to capture relevant information from unstructured or complex data, such as clinical notes that often include patients' medical histories.Such data is largely free-form and unstructured, obtained through diverse clinician-patient interactions.In this work, we present a case study investigating whether variations in clinicians' writing styles can lead to differences in medical context understanding capabilities for pre-trained language models (PLMs) on five downstream tasks related to medical event classification.Our findings indicate that variation in writing style, characterized by linguistic features, can indeed lead to suboptimal performance in deployed NLP models.Furthermore, we explore linguistically guided counterfactual reasoning to mitigate the impact of writing-style variation, suggesting that LLM-based stylistic normalization is effective for this purpose.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166802037