OLMo：加速语言模型科学
OLMo: Accelerating the Science of Language Models
AI总结：
为解决强大语言模型日益封闭、阻碍科学研究的问题，本文发布真正开放的OLMo模型，并公开训练数据、训练与评估代码，以推动语言模型的开放科学研究。
摘要：
语言模型（LMs）已在自然语言处理研究和商业产品中变得无处不在。随着其商业重要性急剧上升，最强大的模型已变得封闭，被专有接口所限制，其训练数据、架构和开发的重要细节均未公开。鉴于这些细节对于科学研究这些模型（包括其偏见和潜在风险）的重要性，我们认为研究界必须能够获得强大且真正开放的语言模型。为此，我们构建了OLMo，一个具有竞争力且真正开放的语言模型，以支持对语言模型的科学研究。与大多数先前仅发布模型权重和推理代码的工作不同，我们发布OLMo的同时公开了训练数据以及训练和评估代码。我们希望这一发布能够赋能开放研究社区，并激发新一轮的创新浪潮。
英文摘要：
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
