KHU_LDI at BioLaySumm2025: Fine-tuning and Refinement for Lay Radiology Report Generation

Though access to one's own radiology reports has improved over the years, the use of complex medical terms makes understanding these reports difficult.To tackle this issue, we explored two approaches: supervised fine-tuning opensource large language models using QLoRA, and refinement, which improves a given generated output using feedback generated by a feedback model.Despite the fine-tuned model outperforming refinement on the test data, refinement showed good results on the validation set, thus showing good potential in the generation of lay radiology reports.Our submission achieved 2nd place in the open track of Subtask 2.1 of the BioLaySumm 2025 shared task.