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Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs

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arxiv 2406.11780 v1 pith:TPY4YTKN submitted 2024-06-17 cs.LG cs.AIcs.CL

Swanand Ravindra Kadhe , Farhan Ahmed , Dennis Wei , Nathalie Baracaldo , Inkit Padhi This is my paper
classification cs.LGcs.AIcs.CL
keywords unlearningdatallmsspungeattributesknowledgelanguagemerge
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Large language models (LLMs) have shown to pose social and ethical risks such as generating toxic language or facilitating malicious use of hazardous knowledge. Machine unlearning is a promising approach to improve LLM safety by directly removing harmful behaviors and knowledge. In this paper, we propose "SPlit, UNlearn, MerGE" (SPUNGE), a framework that can be used with any unlearning method to amplify its effectiveness. SPUNGE leverages data attributes during unlearning by splitting unlearning data into subsets based on specific attribute values, unlearning each subset separately, and merging the unlearned models. We empirically demonstrate that SPUNGE significantly improves the performance of two recent unlearning methods on state-of-the-art LLMs while maintaining their general capabilities on standard academic benchmarks.

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Cited by 3 Pith papers

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  1. Representation-Guided Parameter-Efficient LLM Unlearning

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.

  2. Revisiting the Past: Data Unlearning with Model State History

    cs.LG 2025-06 unverdicted novelty 5.0 of 10

    MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.

  3. Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

    cs.CR 2025-07 conditional novelty 4.0 of 10

    A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.

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