  The legality of training language models (LMs) on copyrighted or otherwise
restricted data is under intense debate. However, as we show, model performance
significantly degrades if trained only on low-risk text (e.g., out-of-copyright
books or government documents), due to its limited size and domain coverage. We
present SILO, a new language model that manages this risk-performance tradeoff
during inference. SILO is built by (1) training a parametric LM on Open License
Corpus (OLC), a new corpus we curate with 228B tokens of public domain and
permissively licensed text and (2) augmenting it with a more general and easily
modifiable nonparametric datastore (e.g., containing copyrighted books or news)
that is only queried during inference. The datastore allows use of high-risk
data without training on it, supports sentence-level data attribution, and
enables data producers to opt out from the model by removing content from the
store. These capabilities can foster compliance with data-use regulations such
as the fair use doctrine in the United States and the GDPR in the European
Union. Our experiments show that the parametric LM struggles on domains not
covered by OLC. However, access to the datastore greatly improves out of domain
performance, closing 90% of the performance gap with an LM trained on the Pile,
a more diverse corpus with mostly high-risk text. We also analyze which
nonparametric approach works best, where the remaining errors lie, and how
performance scales with datastore size. Our results suggest that it is possible
to build high quality language models while mitigating their legal risk.
