BLOOM:一个176B参数的开放获取多语言语言模型
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
AI总结:
BLOOM是一个176B参数的开放获取多语言语言模型,通过数百名研究者合作在ROOTS语料库上训练,并在多任务提示微调后展现出有竞争力的性能,以旨在促进LLMs的民主化应用。
摘要:
大型语言模型(LLMs)已被证明能够基于少量示例或自然语言指令执行新任务。尽管这些能力导致了广泛的采用,但大多数LLMs由资源丰富的组织开发,并且经常对公众保密。作为迈向民主化这一强大技术的一步,我们提出了BLOOM,这是一个176B参数的开放获取语言模型,由数百名研究人员合作设计和构建。BLOOM是一个仅解码器的Transformer语言模型,在ROOTS语料库上训练,ROOTS语料库,该数据集包含46种自然语言和13种编程语言(共59种)的数百个来源。我们发现BLOOM在广泛的基准测试中取得了有竞争力的性能,在经历多任务提示微调后获得了更强的结果。为了促进未来使用LLMs的研究和应用,我们在负责任AI许可证下公开发布了我们的模型和代码。
英文摘要:
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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