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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
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…