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Rethinking Machine Unlearning for Large Language Models

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arxiv 2402.08787 v6 pith:OKQU26RA submitted 2024-02-13 cs.LG cs.CL

Sijia Liu , Yuanshun Yao , Jinghan Jia , Stephen Casper , Nathalie Baracaldo , Peter Hase , Yuguang Yao , Chris Yuhao Liu
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Xiaojun Xu Hang Li Kush R. Varshney Mohit Bansal Sanmi Koyejo Yang Liu
This is my paper
classification cs.LGcs.CL
keywords unlearningllmsmodelapplicationsassessmentessentialexploreinfluence
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We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities, while maintaining the integrity of essential knowledge generation and not affecting causally unrelated information. We envision LLM unlearning becoming a pivotal element in the life-cycle management of LLMs, potentially standing as an essential foundation for developing generative AI that is not only safe, secure, and trustworthy, but also resource-efficient without the need of full retraining. We navigate the unlearning landscape in LLMs from conceptual formulation, methodologies, metrics, and applications. In particular, we highlight the often-overlooked aspects of existing LLM unlearning research, e.g., unlearning scope, data-model interaction, and multifaceted efficacy assessment. We also draw connections between LLM unlearning and related areas such as model editing, influence functions, model explanation, adversarial training, and reinforcement learning. Furthermore, we outline an effective assessment framework for LLM unlearning and explore its applications in copyright and privacy safeguards and sociotechnical harm reduction.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs

    cs.LG 2026-07 conditional novelty 6.5 of 10

    After multi-prompt multi-task fine-tuning, further training on the worst concept-predicting prompt type yields stronger concept unlearning and higher main-task accuracy than uniform multi-prompt or recent baselines.

  2. Understanding Machine Unlearning Through the Lens of Mode Connectivity

    cs.LG 2026-07 unverdicted novelty 6.0 of 10

    Unlearned models usually connect to their originals by smooth low-loss paths, and the smoothness of that path can predict how hard the unlearning task was.

  3. A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning

    cs.LG 2026-01 conditional novelty 6.0 of 10

    A circuit-similarity score predicts which samples an LLM unlearning method will fail to erase, with hard samples relying on deeper, output-facing pathways.

  4. Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design

    cs.CR 2025-08 conditional novelty 6.0 of 10

    Water4MU tunes an invisible watermark on data so that machine unlearning algorithms can remove requested images more effectively, beating prior methods on 'challenging forgets'.

  5. What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests

    cs.CL 2025-07 conditional novelty 6.0 of 10

    WikiMem, a Wikidata-derived canary dataset and a calibrated NLL-ranking metric, identifies which human-fact associations an LLM has memorized, with higher rates for famous people and larger models.

  6. LLM Unlearning Should Be Form-Independent

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Existing LLM unlearning is form-dependent; the new ORT benchmark measures this, and the training-free ROCR edit reduces it by redirecting concept representations.

  7. Certified Unlearning for Neural Networks

    cs.LG 2025-06 reject novelty 6.0 of 10

    Noisy fine-tuning with gradient or model clipping on retained data provably removes the influence of forget data, with guarantees that need no smoothness or convexity assumptions.

  8. Module-Aware Parameter-Efficient Machine Unlearning on Transformers

    cs.LG 2025-08 conditional novelty 5.0 of 10

    MAPE-Unlearn uses Fisher-information-based scores and greedy search to select important heads and filters, then applies sparse unlearning updates, claiming improved efficacy-fidelity trade-offs on Transformers.

  9. SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks

    cs.CR 2025-06 conditional novelty 5.0 of 10

    SOFT paraphrases low-loss fine-tuning samples before training, reducing MIA AUC from about 0.82 to about 0.54 across six datasets at roughly 7% perplexity cost.

  10. SoK: Machine Unlearning for Large Language Models

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A new taxonomy for LLM unlearning distinguishes removal-intended from suppression-intended methods, and argues that gradient ascent methods functionally behave like suppression.

  11. Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A position paper urging a shift from data-tracing to knowledge-tracing machine unlearning for foundation models, supported by a CLIP case study that shows current methods struggle to generalize.

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