most citedMed42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

14 citations · 26 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20242 cited

Named Clinical Entity Recognition Benchmark

Wadood M Abdul, Marco AF Pimentel, Muhammad Umar Salman +6

This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP…

cs.CL20241 cited

Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs

Clément Christophe, Tathagata Raha, Svetlana Maslenkova +4

Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adaptin…

astro-ph.IM20241 cited

L-band nulling interferometry at the VLTI with Asgard/NOTT: status and plans

Denis Defrère, Romain Laugier, Marc-Antoine Martinod +43

NOTT (formerly Hi-5) is the L'-band (3.5-4.0~microns) nulling interferometer of Asgard, an instrument suite in preparation for the VLTI visitor focus. The primary scientific object…

cs.CL202414 cited

Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

Clément Christophe, Praveen K Kanithi, Prateek Munjal +13

This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the co…

astro-ph.IM20228 cited

L-band nulling interferometry at the VLTI with Asgard/Hi-5: status and plans

Denis Defrère, Azzurra Bigioli, Colin Dandumont +34

Hi-5 is the L'-band (3.5-4.0 m) high-contrast imager of Asgard, an instrument suite in preparation for the visitor focus of the VLTI. The system is optimized for high-contrast a…