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Atyaephyra at SemEval-2025 Task 4: Low-Rank Negative Preference Optimization

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arxiv 2503.13690 v2 pith:75WDI6X3 submitted 2025-03-17 cs.CL cs.AIcs.LG

Jan Bronec (1) , Jindřich Helcl (1) ((1) Charles University , Faculty of Mathematics , Physics , Institute of Formal , Applied Linguistics) This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords taskapproachlow-ranknegativeoptimizationpreferencesharedunlearning
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We present a submission to the SemEval 2025 shared task on unlearning sensitive content from LLMs. Our approach employs negative preference optimization using low-rank adaptation. We show that we can utilize this combination to efficiently compute additional regularization terms, which help with unlearning stabilization. The results of our approach significantly exceed the shared task baselines.

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  1. Learning-Time Encoding Shapes Unlearning in LLMs

    cs.CL 2025-06 conditional novelty 6.0 of 10

    How knowledge is encoded during LLM fine-tuning strongly affects later unlearning: paraphrased training data helps unlearning, while entangled chunks hinder selective forgetting.

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