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

Occurrence of Volatile and Semi-Volatile Organic Pollutants in the Russian Arctic Atmosphere: The International Siberian Shelf Study Expedition (ISSS-2020)

Environmental issues in the Arctic region are of primary importance due to the fragility of the Arctic ecosystem. Mainly persistent organic compounds are monitored in the region by nine stationary laboratories. Information on the volatile (VOC) and semi volatile (SVOC) organic priority pollutants is very limited, especially for the Russian Arctic. Air samples from 16 sites along the Russian Arctic coast from the White Sea to the East Siberian Sea were collected on sorption tubes packed with Tenax, Carbograph, and Carboxen sorbents with different selectivity for a wide range of VOCs and SVOCs in 2020 within the framework of the International Siberian Shelf Study Expedition on the research vessel Akademik Keldysh. Thermal desorption gas chromatography–high-resolution mass spectrometry with Orbitrap was used for the analysis. Eighty-six VOCs and SVOCs were detected in the air samples at ng/m3 levels. The number of quantified compounds varied from 26 to 66 per sample. Benzoic acid was the major constituent, followed by BTEX, phenol, chloroform, bis(2-ethylhexyl) phthalate, and carbon tetrachloride. The study allowed for obtaining the first ever data on the presence of 138 priority pollutants in the air of Russian Arctic, whereas the thorough assessment of their possible sources will be the aim of a next investigation

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