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

Exploring “The Healthy Immigrant Effect” Among Elderly Asians with Cancer: A Nationwide Population-Based Assessment

Asians are the fastest-growing immigrant group in the U.S. Asian Americans are also, on average, the oldest immigrant group and so are at heightened risk of cancer and other diseases that are more prevalent in the elderly population. Although a health advantage among Latino immigrants is well documented, there has been less evidence of this “healthy immigrant effect” (HIE) among Asians as a whole or in specific subgroups of Asians, and almost none focused on those who are elderly or those with a cancer diagnosis. This study examines the evidence for a healthy immigrant effect in a large ethnically diverse sample of elderly persons with a cancer diagnosis. This is a retrospective observational study utilizing the Surveillance Epidemiology and End Results –Medicare Health Outcomes Survey (SEER-MHOS). Descriptive, bivariate, and multivariate analyses are used to examine HIE among Latinos and Asians in the aggregate and among subgroups of Asians for five health outcomes: smoking, cancer type, stage of cancer, body mass index (BMI), and self-reported health (SRH). Nativity effects were estimated using survey language and ethnic concentration as proxies. Asians living in an ethnic enclave had lower smoking prevalence, lower BMI, a lower likelihood of a non-endocrine diagnosis, and late-stage cancer diagnosis. A parallel analysis among Latinos living in ethnic enclaves and those who answered surveys in Spanish had lower odds of smoking, non-endocrine cancer, and late-stage diagnosis, consistent with the HIE. No differences consistent with the HIE were found for Chinese respondents in relation to smoking prevalence, cancer type, or stage; however, Chinese respondents opting to complete surveys in their native language or living in ethnic enclaves were less likely to be overweight. The HIE prediction was contradicted by the poorer SRH of Latinos, Asians respondents who were more likely to be first-generation immigrants. Thus, support for the HIE was largely consistent for Asians and Latinos with respect to both language and ethnic concentration. However, there was little evidence of an HIE among Chinese immigrants. Future research to identify the HEI among Asians should use multiple health outcomes and take account of subgroup differences

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