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InContext Unlearning: Language Models as FewShot UnlearnersMartin Pawelczyk 1 Seth Neel * 1 Himabindu Lakkaraju * 1AbstractStandard Unlearning Paradigm Personal Information Name: Alice Net worth:
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What is in-context unlearning language models?
In-context unlearning refers to a mechanism in language models that allows them to erase or modify learned information based on new inputs or instructions, effectively allowing the model to unlearn certain knowledge without retraining.
How to fill out in-context unlearning language models?
Filling out in-context unlearning involves specifying the context in which the model should unlearn certain information, usually by providing examples or directives that indicate what should be forgotten or altered.
What is the purpose of in-context unlearning language models?
The purpose of in-context unlearning is to improve the adaptability and privacy of language models, allowing them to respond to new contexts and requirements while maintaining compliance with ethical standards and user preferences.
What information must be reported on in-context unlearning language models?
Information that must be reported includes the specific data or knowledge being unlearned, the context under which unlearning occurs, the rationale for the unlearning, and any implications for model behavior moving forward.
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