BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Implementation of a roadmap for the comprehensive diagnosis, follow-up, and research of childhood leukemias in vulnerable regions of Mexico: results from the PRONAII Strategy

The main objective of the National Project for Research and Incidence of Childhood Leukemias is to reduce early mortality rates for these neoplasms in the vulnerable regions of Mexico. This project was conducted in the states of Oaxaca, Puebla, and Tlaxcala. A key strategy of the project is the implementation of an effective roadmap to ensure that leukemia patients are the target of maximum benefit of interdisciplinary collaboration between researchers, clinicians, surveyors, and laboratories. This strategy guarantees the comprehensive management of diagnosis and follow-up samples of pediatric patients with leukemia, centralizing, managing, and analyzing the information collected. Additionally, it allows for a precise diagnosis and monitoring of the disease through immunophenotype and measurable residual disease (MRD) studies, enhancing research and supporting informed clinical decisions for the first time in these regions through a population-based study. This initiative has significantly improved the diagnostic capacity of leukemia in girls, boys, and adolescents in the regions of Oaxaca, Puebla, and Tlaxcala, providing comprehensive, high-quality care with full coverage in the region. Likewise, it has strengthened collaboration between health institutions, researchers, and professionals in the sector, which contributes to reducing the impact of the disease on the community

Mexican Teachers’ Perceptions of Teaching English through Content Based Instruction in the State of Guanajuato Mexico: A Dual Perspective

Este artículo se centra en la percepción de profesores mexicanos de inglés sobre la enseñanza del idioma basada en contenidos. Los profesores, objeto de estudio, tienen una perspectiva dual: aprendieron el idioma inglés a través de contenido y ahora enseñan inglés aplicando el mismo método de enseñanza. Se utilizó una metodología con enfoque cualitativo; se aplicaron entrevistas semi-estructuradas a los participantes con el fin de entender sus experiencias y percepciones sobre el tema. Las entrevistas se centran en la percepción de los docentes en relación con la instrucción basada en contenidos y determinan si los participantes recomendarían o no el método. Los resultados sugieren que a pesar de haber aprendido inglés a través de contenidos, los participantes no lo recomendarían debido al contexto en el que se encuentran actualmente. Recomendaciones para futuras investigaciones son presentadas en las conclusiones

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License