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This research aims to explore machine unlearning techniques in supervised learning, particularly within natural language processing contexts.

March 25, 2026Future InternetOpen Access

Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation

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This research aims to explore machine unlearning techniques in supervised learning, particularly within natural language processing contexts.
Conducted a systematic review of machine unlearning techniques based on PRISMA guidelines.
Developed a multi-level taxonomy categorizing unlearning approaches by exactness, granularity, and constraints.
Performed a benchmark evaluation using DistilBERT on a dataset of news headlines to assess unlearning effectiveness.
Identified trade-offs between model accuracy, computational cost, and effectiveness of unlearning.
Found that simply removing data does not eliminate its influence on models as intended.
Demonstrated the importance of selecting appropriate unlearning techniques based on deployment scenarios.

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This work evaluates machine unlearning techniques in natural language processing, highlighting trade-offs in accuracy and privacy implications.

Cosentino et al. (2026) studied this question.

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This work evaluates machine unlearning techniques in natural language processing, highlighting trade-offs in accuracy and privacy implications.