4 papers
Less Finetuning, Better Retrieval: Rethinking LLM Adaptation for Biomedical Retrievers via Synthetic Data and Model Merging
Sameh Khattab, Jean-Philippe Corbeil, Osman Alperen KoraÅ +5
Retrieval-augmented generation (RAG) has become the backbone of grounding Large Language Models (LLMs), improving knowledge updates and reducing hallucinations. Recently, LLM-based…
Automatic Fine-grained Segmentation-assisted Report Generation
Frederic Jonske, Constantin Seibold, Osman Alperen Koras +6
Reliable end-to-end clinical report generation has been a longstanding goal of medical ML research. The end goal for this process is to alleviate radiologists' workloads and provid…
Towards Conditioning Clinical Text Generation for User Control
Osman Alperen KoraÅ, Rabi Bahnan, Jens Kleesiek +1
Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations an…
MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters
Amin Dada, Osman Alperen Koras, Marie Bauer +4
While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large langu…