Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages

Understand

Large Language Models (LLMs) like GPT-4 and LLaMA have shown incredible proficiency at natural language processing tasks and have even begun to excel at tasks across other modalities such as vision and audio.

• Despite their success, LLMs often struggle to perform well on low-resource languages because there is so little training data available.
• This shortcoming is especially prevalent with open source models.
• In this work, we explore training LLaMA-2 to speak Amharic, a language which is spoken by over 50 million people world wide, but has orders of magnitude less data available than languages like English.