  Language models (LM or LLM) are increasingly deployed in the field of
artificial intelligence (AI) and its applications, but the question arises as
to whether they can be a common resource managed and maintained by a community
of users. Indeed, the dominance of private companies with exclusive access to
massive data and language processing resources can create inequalities and
biases in LM, as well as obstacles to innovation for those who do not have the
same resources necessary for their implementation. In this contribution, we
examine the concept of the commons and its relevance for thinking about LM. We
highlight the potential benefits of treating the data and resources needed to
create LMs as commons, including increased accessibility, equity, and
transparency in the development and use of AI technologies. Finally, we present
a case study centered on the Hugging Face platform, an open-source platform for
deep learning designed to encourage collaboration and sharing among AI
designers.
