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

Comparing national approaches to the study of intelligence

This forum compares and contrasts national experiences in the development of intelligence studies from the perspective of seven countries: France, Japan, Israel, Romania, Spain, the United Kingdom, and the United States. The discussion is structured around a comparative framework that emphasizes five core dimensions that, we posit, are essential to the emergence of this subfield: access to relevant government information, institutionalization of research on intelligence and security in a higher education setting, periodic scientific meetings and networks, teaching and learning opportunities, and engagement between researchers and practitioners. The forum demonstrates how researchers working in different contexts and disciplines have overcome similar challenges to broaden our understanding of secret government practices