title: SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
summary: 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.
paper_authors._id: 64d312fbe9955005b0b8846d
paper_authors.name: Sewon Min
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-08-09T10:51:57.100Z
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paper_authors.fullname: Sewon Min
paper_authors.type: user
paper_authors.user: sewon
paper_authors._id: 64d312fbe9955005b0b8846e
paper_authors.name: Suchin Gururangan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-09T11:29:46.822Z
paper_authors._id: 5e9272254957053f60648a01
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paper_authors.fullname: Suchin Gururangan
paper_authors.type: user
paper_authors.user: suching
paper_authors._id: 64d312fbe9955005b0b8846f
paper_authors.name: Eric Wallace
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-08-09T04:15:56.143Z
paper_authors._id: 63112d2431257261d20d5754
paper_authors.avatarUrl: /avatars/502a68c0fb2f0c6989fe2869d0a7e3f4.svg
paper_authors.fullname: Eric Wallace
paper_authors.type: user
paper_authors.user: EricWallace
paper_authors._id: 64d312fbe9955005b0b88470
paper_authors.name: Hannaneh Hajishirzi
paper_authors._id: 64d312fbe9955005b0b88471
paper_authors.name: Noah A. Smith
paper_authors._id: 64d312fbe9955005b0b88472
paper_authors.name: Luke Zettlemoyer
paper_title: SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore
paper_summary: 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.
paper_ai_summary: SILO is a language model that uses a parametric LM trained on a public domain corpus and a nonparametric datastore to improve performance on diverse text while mitigating legal risks.
paper_ai_keywords: Open License Corpus parametric LM nonparametric datastore sentence-level data attribution GDPR fair use doctrine Pile