{"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/debiasing-algorithm-through-model-adaptation","title":"Debiasing Algorithm through Model Adaptation","arxiv_id":"2310.18913","date":"2023-10-29","proceeding":null,"authors":["Tomasz Limisiewicz","David Mareček","Tomáš Musil"],"abstract":"Large language models are becoming the go-to solution for the ever-growing number of tasks. However, with growing capacity, models are prone to rely on spurious correlations stemming from biases and stereotypes present in the training data. 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