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.
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可解释的人工智能（XAI）中方法的动机通常包括检测，量化和缓解偏见，并为使机器学习模型更加公平而做出贡献。但是，确切的XAI方法可以如何帮助打击偏见。在本文中，我们简要回顾了NLP研究中的解释性和公平性的趋势，确定了当前的实践，其中采用了解释性方法来检测和减轻偏见，并调查了阻止XAI方法在解决公平问题中更广泛使用的障碍。
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