{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/non-vacuous-generalization-bounds-for-large","title":"Non-Vacuous Generalization Bounds for Large Language Models","arxiv_id":"2312.17173","date":"2023-12-28","proceeding":null,"authors":["Sanae Lotfi","Marc Finzi","Yilun Kuang","Tim G. J. Rudner","Micah Goldblum","Andrew Gordon Wilson"],"abstract":"Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply parrot their training corpora. 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