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Summary

Description
English: An overview of the SILO system from the paper "SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore", corresponding to figure 1 there, whose caption reads: "[...] We train a parametric language model on low-risk datasets that contain public domain text (e.g., copyright-expired books) and permissively licensed code. At inference time, we use a nonparametric datastore that can include high-risk data, including medical text with personally-identifiable information, copyrighted news, copyrighted books, data requiring attribution, and code under non-permissive licenses (counterclockwise from the top of figure). The datastore can be modified at any time, e.g., to respond to opt-out requests."
Date
Source https://github.com/kernelmachine/silo-lm
Author Sewon Min, Suchin Gururangan, Eric Wallace, Hannaneh Hajishirzi, Noah A. Smith, and Luke Zettlemoyer

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8 August 2023

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current01:16, 24 June 2025Thumbnail for version as of 01:16, 24 June 20251,080 × 540 (66 KB)HaeBUploaded a work by Sewon Min, Suchin Gururangan, Eric Wallace, Hannaneh Hajishirzi, Noah A. Smith, and Luke Zettlemoyer from https://github.com/kernelmachine/silo-lm with UploadWizard

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