Domain-Specific Fine-Tuning of Large Language Models for Pharmacological Question Answering - Verol, Felipe; Regino, Andre Gomes; Zagatti, Fernando Rezende; Rosa, Ferrucio de Franco; Reis, Julio Cesar Dos; Bonacin, Rodrigo (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Large Language Models (LLMs) perform well in general NLP tasks but face challenges in specialized domains such as pharmacology. This study investigates whether fine-tuning with DrugBank data improves response reliability. We construct a question–answer dataset from absorption and metabolism sections and fine-tune a LLaMA 3.1 8B model using efficient adaptation techniques. The effectiveness of the fine-tuned model is evaluated against its original version using ROUGE-L, BLEU, and Exact Match metrics, as well as qualitative analysis. The results show improvements and more domain-specific responses, indicating that fine-tuning effectively adapts LLMs to pharmacological tasks.