美国安全与新兴技术中心_开放模型在研究中的应用

Issue BriefOctober 2025The Use of%Open Models%in ResearchAuthor=Kyle Mille1Mia Hoffman.Rebecca GellesThe Use of%Open Models%in ResearchAuthor=Kyle Mille1Mia Hoffman.Rebecca Gelles Center for Security and Emerging Technology | 1 Executive Summary There is widespread consensus that open and freely available AI models benefit research. Yet there is a lack of empirical evidence detailing how this relationship manifests. This report aims to fill this gap by investigating the use of open large language models (LLMs) in published research, overviewing what organizations and countries use them most frequently, and considering their wider impact on research. To this end, we identify and analyze more than 250 publications that use open models in ways that require access to model weights, and derive a taxonomy of use cases that openly available model weights exclusively or predominantly enable. We then review more than 130 publications that use closed models to compare use cases when model weights are and are not openly available. Our analysis finds that open models enable a more diverse range of use cases than closed models. Of the eight high-level use cases for AI models we identified, five are exclusively enabled by access to model weights, two predominantly require weights, and one does not require weights. Those requiring weights include continuously pretraining models to expand their general knowledge, compressing models to improve their efficiency, combining different models or synchronizing their modalities (e.g., text and imagery), and measuring the functionality of models on hardware or the performance of hardware when running models. Two use cases predominantly require access to weights: fine-tuning models for particular tasks or domains, and examining model internals to interpret their functionality. While some closed model application programming interfaces (API) allow for these use cases, the access offered is generally very limited and does not, for example, allow for customized fine-tuning or granular examination of model internals. These APIs are therefore generally less useful to researchers for these use cases, and most studies assessed in this report that conducted model fine-tuning or examination required access to model weights. The final use case is prompting, which we define as any form of input-output probing. Prompting allows for the evaluation of model performance, capabilities, alignment, and safety, among other things, and requires only minimal access to a model through a web or programming interface, so it can be conducted on both open and closed models. In our sample of papers that used closed models, researchers engaged almost exclusively in model prompting. Center for Security and Emerging Technology | 2 These open model use cases allow researchers to investigate a wider range of questions, explore more avenues of experimentation, and implement and demonstrate a wider range of techniques than if they only had access t