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

Feminist Aesthetics and Art

Contemporary challenges to feminist aesthetics, discourse and the practice of feminist art

Efficiency and mergers in English higher education 1996/97 to 2008/09:parametric and non-parametric estimation of the multi-input multi-output distance function

This paper explores the issue of efficiency in English higher education using data envelopment analysis and stochastic frontier analysis to estimate an output distance function (which incorporates measures of both quantity and quality of teaching and research inputs and outputs) over a thirteen-year period. The study compares the efficiency estimates derived from various estimation methods, and uses the results to provide guidance to researchers, managers and policy-makers on undertaking efficiency studies. The length of the study under consideration allows a preliminary statistical investigation of the effects on efficiency of merger activity in higher education