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arXiv.orgNot All Tokens Are Meant to Be ForgottenLarge Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they tend to memorize unwanted information, such as private or copyrighted content, raising significant privacy and legal concerns. Unlearning has emerged as a promising solution, but existing methods face a significant challenge of over-forgetting. This issue arises because they indiscriminately suppress the generation of all the tokens in forget samples, leading to a substantial loss of model utility. To overcome this challenge, we introduce the Targeted Information Forgetting (TIF) framework, which consists of (1) a flexible targeted information identifier designed to differentiate between unwanted words (UW) and general words (GW) in the forget samples, and (2) a novel Targeted Preference Optimization approach that leverages Logit Preference Loss to unlearn unwanted information associated with UW and Preservation Loss to retain general information in GW, effectively improving the unlearning process while mitigating utility degradation. Extensive experiments on the TOFU and MUSE benchmarks demonstrate that the proposed TIF framework enhances unlearning effectiveness while preserving model utility and achieving state-of-the-art results.

@iFSR @dm2lct @BitsUndBaeumeDresden
Wir haben während der Scheindebatte zu #Polyas auch mal eine kleine Scheinwahl mit Polyas veranstaltet.
Nach anfänglichen Problemen, das Wählerverzeichnis in eine Excell (echt so) Datei zu überführen, haben alle Teilnehmenden eine signierte, aber unverschlüsselte Mail mit Passwörtern von Polyas erhalten.
Man kann zwangsläufig auch ungültig wählen (auch in Kombination mit einem gültigen Kreuz).
Am Ende erhalten Nutzende zur Bestätigung einen Grünen Haken. Die Verifikation der #Blockchain ist ein Feature, welches man sich dazu kaufen müsste. Es ist auch nur in der neusten, vom BSI nicht zertifizierten Version enthalten.