Contrastive Representation Shaping for LLM Unlearning

Abstract:Most LLM unlearning methods aim to approximate retrain-from-scratch behaviors with minimal distribution shift, often via alignment-style objectives defined in the prediction space. While effective at reducing forgotten content generation, such approaches may act as suppression: forgotten concepts can persist in representations and remain entangled with retained knowledge. We introduce CLReg, a contrastive representation regularizer that identifies forget features while pushing them away from retain features, reducing forget--retain interference while empirically preserving the scale and shape of retain features. As light motivation for the mechanism, we provide a one-step analysis showing that CLReg decreases a simple entanglement proxy in the embedding space. Across unlearning benchmarks and LLMs of different sizes, CLReg decreases forget-retain representation entanglement to enhance mainstream unlearning methods without extra privacy risks, inspiring future unlearning work to remove forget concepts via representation shaping. Code is available at this https URL.

Machine Learning (cs.LG)

arXiv:2601.22028 [cs.LG]

(or arXiv:2601.22028v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2601.22028 Focus to learn more arXiv-issued DOI via DataCite

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