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Research questionHow can LLMs remove undesirable knowledge while preserving utility with limited retention and unlearning data?Removing targeted knowledge from a pretrained language model can damage unrelated capabilities, while preserving those capabilities often requires substantial retention or unlearning data. The practical challenge is balancing reliable forgetting against model utility when such data are limited.
AI
Alignment & Safety
Evaluation & Benchmarks
LLM Pretraining & Post-training
Machine Learning
Mechanistic Interpretability
Natural Language Processing
Latest papersRecent research connected to this question, newest first.Entangled Representations Amplify Collateral Damage in UnlearningThe evidence comes from six 254M-parameter language models trained on English Wikipedia, with graded disentanglement between biology and non-biology knowledge. Three standard unlearning methods were applied to each model; the intervention changed model representations rather than the data or unlearning algorithms.research paper · Sep 2, 2026CALIBURN: Self-Calibrated LLM Unlearning AlignmentThe source concerns pretrained language models undergoing undesirable-knowledge unlearning, with evidence from evaluations on MUSE and WMDP. It reports results on knowledge removal, utility preservation, and dependence on retention or unlearning data, but does not specify a deployment setting or access constraints.research paper · Sep 2, 2026
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