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

Upgraded rotary harrow for vineyards

To improve the quality of surface loosening, it is necessary to search for new approaches to soil maintenance and solving problems related to the reproduction of soil fertility, maintaining production volumes. In this connection, in order to increase operational reliability, simplify the design and clean the teeth from weed stalks in the basic technical tool, which includes a frame in the form of a beam equipped with a suspension system with mounted teeth. The teeth are made of a rectangular strip section, with a bend, with an oval surface on the side opposite to the direction of the bend and a formed arrow-shaped tip. They are placed on batteries of rigidly fixed disks. The teeth have reinforcing annular plates, which are located on the free length of the teeth, and their width is made at least equal to the width of the teeth. The cam element is made in the form of a cylindrical ring equipped with a rounded recess. The use of a modernized rotary harrow in the inter-row spacing of vine-yards will increase the operational reliability and quality of surface loosening, as well as simplify the design and clean the teeth from weed stalks

Dataset debt in biomedical language modeling

Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP. However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP's significant dataset debt - the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale. To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets. We find that only 13% of datasets are available via programmatic access and 30% lack any documentation on licensing and permitted reuse. Our dataset catalog is available at: https://tinyurl.com/bigbio22