1. AI model disgorgement: Methods and choices.
- Author
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Achille, Alessandro, Kerns, Michael, Klingenberg, Carson, and Soatto, Stefano
- Subjects
- *
MACHINE learning , *GENERATIVE artificial intelligence , *LANGUAGE models , *ARTIFICIAL intelligence , *INTELLECTUAL property - Abstract
Over the past few years, machine learning models have significantly increased in size and complexity, especially in the area of generative AI such as large language models. These models require massive amounts of data andcomputecapacitytotrain,totheextentthatconcerns over the training data (such as protected or private content)cannotbepracticallyaddressedbyretrainingthe model"fromscratch"withthequestionabledataremoved or altered. Furthermore, despite significant efforts and controls dedicated to ensuring that training corpora are properly curated and composed, the sheer volume re- quiredmakesitinfeasibletomanuallyinspecteachdatum comprising a training corpus. One potential approach to training corpus data defects is model disgorgement, by which we broadly mean the elimination or reduction of not only any improperly used data, but also the effects of improperly used data on any component of an ML model. Model disgorgement techniques can be used to address a wide range of issues, such as reducing bias or toxicity, increasing fidelity, and ensuring responsible use of intellectual property. In this paper, we survey the land- scape of model disgorgement methods and introduce a taxonomyofdisgorgementtechniquesthatareapplicable to modern ML systems. In particular, we investigate the variousmeaningsof"removingtheeffects"ofdataonthe trained model in a way that does not require retraining from scratch [ABSTRACT FROM AUTHOR]
- Published
- 2024
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