Nicholas Taylor
Deputy Group Leader for Technology Strategy and Services
Los Alamos National Laboratory
The advent of large language models has lately and precipitously increased the demand for scientific publications for artificial intelligence (AI) training, fine-tuning, and retrieval-augmented generation. However, these use cases are frequently proscribed by the terms of institutional content access licenses, if not by unsettled copyright law more generally. While market solutions are slowly emerging and appellate court precedents are still pending, research libraries may, in the meantime, help their user communities locate fungible or alternative resources that are unencumbered for AI use cases, under frameworks such as open access, Creative Commons, Government Use Right, Fair Use, etc. This lightning talk will briefly cover how the Research Library at Los Alamos National Laboratory is supporting sudden and ambitious demands for scientific publications as data for new AI initiatives by facilitating the discovery of unencumbered works.
