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
{"title":"Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models","authors":"Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini","doi":"10.1145/3703454","DOIUrl":null,"url":null,"abstract":"This survey summarizes the most recent methods for building and assessing <jats:italic>helpful, honest, and harmless</jats:italic> 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.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3703454","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Causal Discovery from Temporal Data: An Overview and New Perspectives Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges Racial Bias within Face Recognition: A Survey A Survey of Machine Learning for Urban Decision Making: Applications in Planning, Transportation, and Healthcare Tool Learning with Foundation Models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1