Foundation models (FM) have demonstrated remarkable performance across a wide range of tasks (especially in the fields of natural language processing and computer vision), primarily attributed to their ability to comprehend instructions and access extensive, high-quality data. This not only showcases their current effectiveness but also sets a promising trajectory towards the development of artificial general intelligence. Unfortunately, due to multiple constraints, the raw data of the model used for large model training are often inaccessible, so the use of end-to-end models for downstream tasks has become a new research trend, which we call Learn From Model (LFM) in this article. LFM focuses on the research, modification, and design of FM based on the model interface, so as to better understand the model structure and weights (in a black box environment), and to generalize the model to downstream tasks. The study of LFM techniques can be broadly categorized into five major areas: model tuning, model distillation, model reuse, meta learning and model editing. Each category encompasses a repertoire of methods and strategies that aim to enhance the capabilities and performance of FM. This paper gives a comprehensive review of the current methods based on FM from the perspective of LFM, in order to help readers better understand the current research status and ideas. To conclude, we summarize the survey by highlighting several critical areas for future exploration and addressing open issues that require further attention from the research community. The relevant papers we investigated in this article can be accessed at <https://github.com/ruthless-man/Awesome-Learn-from-Model>.

翻译：基础模型（Foundation Models, FM）已在广泛任务中展现出卓越性能（尤其在自然语言处理和计算机视觉领域），这主要归功于其理解指令的能力及对大规模高质量数据的访问。这不仅彰显了当前的有效性，也为通用人工智能的发展铺就了前景光明的道路。然而，由于多重限制，用于大模型训练的原始数据往往难以获取，因此利用端到端模型处理下游任务成为新的研究趋势，本文将其称为“模型学习”（Learn From Model, LFM）。LFM专注于基于模型接口对FM进行研究、修改与设计，以更深入地理解模型结构与权重（在黑盒环境下），并将模型推广至下游任务。LFM技术的研究可大致分为五大领域：模型微调、模型蒸馏、模型复用、元学习与模型编辑。每个领域均包含一系列旨在增强FM能力与性能的方法与策略。本文从LFM视角出发，对当前基于FM的方法进行了全面综述，旨在帮助读者更好地理解当前研究现状与思路。最后，我们总结该综述，指出未来探索的关键领域，并强调需要研究社区进一步关注的开放性问题。本文所调查的相关论文可在<https://github.com/ruthless-man/Awesome-Learn-from-Model>获取。