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20182026
most citedMed42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

14 citations · 22 across the 5 of their papers we have counts for

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cs.CL2026

BYOL: Bring Your Own Language Into LLMs

Syed Waqas Zamir, Wassim Hamidouche, Boulbaba Ben Amor +3

Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain fundamentally constrained by the severe imbalance in global language resources. While over 7,000 l…

cs.CL2024★ 14 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…