Multilingual LLMs and the Values Divide in AI with Sara Hooker - #651

This article summarizes a podcast episode featuring Sara Hooker, discussing challenges and advancements in multilingual language models (LLMs). Key topics include data quality, tokenization, data augmentation, and preference training. The conversation also touches upon the Mixture of Experts technique, the importance of communication between ML researchers and hardware architects, the societal impact of language models, safety concerns of universal models, and the significance of grounded conversations for risk mitigation. The episode highlights Cohere's work, including the Aya project, an open science initiative focused on building a state-of-the-art multilingual generative language model.

• •Multilingual LLMs face challenges like data quality and tokenization.
• •Data augmentation and preference training are used to address these issues.
• •Communication between ML researchers and hardware architects is crucial for progress.