This survey summarizes the most recent methods for building and assessing helpful, honest, and harmless neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection, and adversarially robust training techniques. Special care is given to evidencing recent progress on value alignment, commonsense reasoning, factuality enhancement, and abstract reasoning of language models. Most reviewed works in this survey publicly shared their code and related data and were accepted in world-leading Machine Learning venues. This work aims to help researchers and practitioners accelerate their entrance into the field of human-centric neural language models, which might be a cornerstone of the contemporary and near-future industrial and societal revolution.

中文翻译：

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开放道德 AI：开源以人为本的神经语言模型的进步


本调查总结了构建和评估有用、诚实和无害的神经语言模型的最新方法，考虑了小型、中型和大型模型。给出了有助于调整预训练模型的开源资源的指针，包括使用参数高效技术的方法、专门的提示框架、适配器模块、特定于案例的知识注入和对抗性稳健的训练技术。特别注意证明语言模型的价值对齐、常识推理、事实性增强和抽象推理方面的最新进展。本次调查中审查最多的作品公开分享了他们的代码和相关数据，并被世界领先的机器学习场所接受。这项工作旨在帮助研究人员和从业者加速进入以人为中心的神经语言模型领域，这可能是当代和不久的将来工业和社会革命的基石。