3 papers
cs.AI2026
Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
Osman Alperen Ãinar-KoraÅ, Marie Bauer, Sameh Khattab +7
Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is…
cs.CL2026
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…
cs.IR2026
AIANO: Enhancing Information Retrieval with AI-Augmented Annotation
Sameh Khattab, Marie Bauer, Lukas Heine +3
The rise of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) has rapidly increased the need for high-quality, curated information retrieval datasets. These dat…