9 citations · 12 across the 6 of their papers we have counts for
6 papers
Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why
Osman Alperen Çinar-Koraş, 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…
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…
Modular Expert Merging for Biomedical Retrieval
Sameh Khattab, Jean-Philippe Corbeil, Osman Alperen Çinar-Koraş +5
Adapting general-purpose LLMs into domain-specialized dense retrievers typically requires large-scale training on mixed-domain data. We show that merging independently trained doma…
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…
Medical Foundation Models are Susceptible to Targeted Misinformation Attacks
Tianyu Han, Sven Nebelung, Firas Khader +9
Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains, holding promising potential for diverse medical applications…