Personalized Text Generation with LLMs: A Systematic Literature Review with a Focus on Healthcare - Caraíba, André Felipe dos S.; Oliveira, Sávio S. T. de; Fernandes, Deborah S. A. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21732, 2026)
Large Language Models (LLMs) produce generic responses that fail to account for individual preferences and contexts. Personalized LLMs address this by leveraging user-specific data to generate tailored outputs. This paper presents a systematic literature review of 52 studies on personalized text generation, published between 2022 and 2025, with focus on healthcare applications.This study follows Kitchenham’s guidelines for planning and conducting systematic literature reviews (SLRs), while the reporting of the review process adheres to the PRISMA framework. The analysis focuses on techniques, models, metrics, and challenges identified in the literature. The results indicate that prompting is the most adopted technique (55.8%), followed by finetuning (46.2%) and RAG (26.9%). Health applications remain underrepresented (9.6%) but are rapidly emerging, covering tasks such as personalized medical responses and mental health assistance. Key challenges include the lack of personalization-aware metrics, static preference assumptions, and privacy concerns.