We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks. We release both the model weights and code, and have also deployed the model on a public web page to interact with organic users. This technical report describes how the model was built (architecture, model and training scheme), and details of its deployment, including safety mechanisms. Human evaluations show its superiority to existing open-domain dialogue agents, including its predecessors (Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for continual learning using the data collected from deployment, which will also be publicly released. The goal of this research program is thus to enable the community to study ever-improving responsible agents that learn through interaction.
翻译:我们介绍了BlenderBot 3, 一种175B参数对话模式,这个模式能够通过互联网和长期记忆进行开放式对话,并且已经接受了关于大量用户界定任务的培训。我们发布了模型重量和代码,并在公共网页上部署了模型,与有机用户互动。本技术报告描述了模型是如何构建的(建筑、模型和培训计划),以及其部署的详细情况,包括安全机制。人类评价显示,它优于现有的开放式对话机构,包括其前身(Roller等人,2021年;Komeili等人,2022年)。最后,我们详细说明了我们利用从部署中收集的数据不断学习的计划,这些数据也将公开发布。因此,本研究方案的目标是使社区能够研究通过互动学习的不断改进的负责任的机构。