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Automatic Text Simplification for French Medical Documents with LLMs: The Role of Target Audience and Genre

Medical information is hard for non-specialists to understand, despite its importance for treatment success. Automatic text simplification (ATS) rewrites complex documents into simpler versions, with effectiveness measured through ATS evaluation metrics and readability metrics. A key challenge in ATS is calibrating simplification to match the reading abilities of specific target audiences, as different populations have different comprehension needs. Since socio-demographic factors such as education level and health literacy are known to correlate with reading abilities, we hypothesize that large language models (LLMs) may be able to adjust their simplification strategies when provided with descriptions of target audiences. In this study, we investigate how LLMs simplify French medical documents when prompted with socio-demographic characteristics of target patients. We compare this approach with prompts based on language proficiency levels (CEFR) to determine whether LLMs respond differently to explicit proficiency levels versus implicit audience descriptions. Our experiments with five LLMs on three types of French medical documents show that CEFR prompts produce greater readability variation (particularly for Llama-3.1-8B), while socio-demographic factors yield more homogeneous outputs. Text genre also considerably impacts LLM outputs for ATS.