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Agent-in-the-loop to distill expert knowledge into artificial intelligence models: a survey

文献类型：期刊论文

作者Gao, Jiayuan2,3; Zhang, Yingwei2,3; Chen, Yiqiang2,3; Dong, Yihan4; Chen, Yuanzhe2,3; Song, Shuchao2,3; Tang, Boshi1; Gu, Yang2,3
刊名ARTIFICIAL INTELLIGENCE REVIEW
出版日期2025-06-04
卷号58期号:9页码:55
关键词Human-in-the-Loop Machine learning Deep learning Large language models
ISSN号0269-2821
DOI10.1007/s10462-025-11255-1
英文摘要Large-scale neural networks have revolutionized many general knowledge areas (e.g., computer vision and language processing), but are still rarely applied in many expert knowledge areas (e.g., healthcare), due to data sparsity and high annotation expenses. Human-in-the-loop machine learning (HIL-ML) incorporates expert domain knowledge into the modeling process, effectively addressing these challenges. Recently, some researchers have started using large models to substitute for certain tasks typically performed by humans. Although large models have limitations in expert knowledge areas, after being trained on trillions of examples, they have demonstrated advanced capabilities in reasoning, semantic understanding, grounding, and planning. These capabilities can serve as proxies of human, which introduces new opportunities and challenges in HIL-ML area. Based on the above, we summarize a more comprehensive framework, Agent-in-the-Loop Machine Learning (AIL-ML), where agent represents both humans and large models. AIL-ML can efficiently collaborate human and large model to construct vertical AI models with lower costs. This paper presents the first review of recent advancements in this area. First, we provide a formal definition of AIL-ML and discuss its related fields. Then, we categorize the AIL-ML methods based on data processing and model development, providing formal definitions for each, and present representative works in detail for each category. Third, we highlight relative applications of AIL-ML. Finally, we summarize the current literature and highlight future research directions.
资助项目Natural Science Foundation of China ; Improvement Project of Chinese Academy of Sciences ; Science and Technology Innovation Program of Hunan Province[2022RC4006] ; Science and Technology Innovation Program of Hunan Province[2024 JJ9031] ; Innovation Funding of ICT, CAS ; [62302487]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001502355300008
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/42319]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Yiqiang
作者单位1.Tsinghua Univ, Beijing, Peoples R China
2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Beijing Inst Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Jiayuan,Zhang, Yingwei,Chen, Yiqiang,et al. Agent-in-the-loop to distill expert knowledge into artificial intelligence models: a survey[J]. ARTIFICIAL INTELLIGENCE REVIEW,2025,58(9):55.
APA Gao, Jiayuan.,Zhang, Yingwei.,Chen, Yiqiang.,Dong, Yihan.,Chen, Yuanzhe.,...&Gu, Yang.(2025).Agent-in-the-loop to distill expert knowledge into artificial intelligence models: a survey.ARTIFICIAL INTELLIGENCE REVIEW,58(9),55.
MLA Gao, Jiayuan,et al."Agent-in-the-loop to distill expert knowledge into artificial intelligence models: a survey".ARTIFICIAL INTELLIGENCE REVIEW 58.9(2025):55.

入库方式： OAI收割

来源：计算技术研究所

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