5 papers
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…
Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs
Amr Abourayya, Jens Kleesiek, Michael Kamp
Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods require transmitting large colle…
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…
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…
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment
Jean-Philippe Corbeil, Amin Dada, Jean-Michel Attendu +7
High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective al…