BLOOM (2022) › The Solution

The technical development of BLOOM involved several key innovations that distinguished it from previous large language models. The model was trained on a diverse, multilingual dataset that included text from 46 languages, with particular attention to underrepresented languages and regions. The training data was carefully curated to ensure high quality and diversity, with efforts made to include content from a wide range of sources and perspectives. The model architecture was based on the transformer design, similar to GPT-3, but with modifications to better handle multilingual text and improve efficiency.

The training process for BLOOM was conducted using the Jean Zay supercomputer in France, with the entire process being documented and made transparent to the research community. The training data, model weights, and training code were all made publicly available, enabling researchers to understand exactly how the model was developed and to build upon the work. This level of transparency was unprecedented for a model of BLOOM's scale and represented a significant advance in open science practices for AI research.