Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-11-06 DOI:10.1145/3703454
Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini
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Abstract

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.
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开放伦理人工智能:以人为本的开源神经语言模型的进展
本调查总结了构建和评估有益、诚实和无害神经语言模型的最新方法,并考虑了小型、中型和大型模型。文中提供了有助于对齐预训练模型的开源资源,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、特定案例知识注入以及对抗性强的训练技术。本调查还特别关注了价值对齐、常识推理、事实性增强和语言模型抽象推理方面的最新进展。本调查报告中的大多数综述作品都公开分享了其代码和相关数据,并被世界领先的机器学习刊物所接受。这项工作旨在帮助研究人员和从业人员加速进入以人为本的神经语言模型领域,这可能是当代和不久将来工业和社会革命的基石。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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