Large Language Models (LLMs), such as GPT-4 and LLaMA, have made incredible strides in natural language processing, and are now extending their reach to handle multimodal tasks involving visual and auditory inputs. However, deploying these powerful models for low-resource languages, like Amharic, which is spoken by over 50 million people worldwide, remains challenging due to limited training data. Researchers have taken on this issue by using translation models for data augmentation, boosting the training dataset from millions to billions of tokens. They’ve connected an image encoder to LLaMA-2, resulting in the creation of a multimodal Amharic LLM that can comprehend both text and images. Their work includes the Amharic adaptation of a benchmark dataset to evaluate the model, all of which has been open-sourced on GitHub.

Key takeaways include:

• Development of LLaMA-2 for Amharic, enhancing it with image understanding capabilities.
• Successful employment of data augmentation to address data scarcity for low-resource languages.
• Creation of a benchmark dataset to assess the model’s performance on Amharic.

The integration of visual information into Amharic LLMs could significantly impact the accessibility of AI technologies for non-English speaking communities, potentially leading to more personalized and inclusive AI-driven applications.