mlsanityData to 14 Oct 2026

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness

arXiv 2506.06112 ↗First posted 6 Jun 2025cs.LG cs.AI cs.CRcited by 11

On the map: LLM safety and security → Machine unlearning

Abstract (arXiv)

Growing concerns over data privacy and security highlight the importance of machine unlearning--removing specific data influences from trained models without full retraining. Techniques like Membership Inference Attacks (MIAs) are widely used to externally assess successful unlearning. However, existing methods face two key limitations: (1) maximizing MIA effectiveness (e.g., via online attacks) requires prohibitive computational resources, often exceeding retraining costs; (2) MIAs, designed for binary inclusion tests, struggle to capture granular changes in approximate unlearning. To address these challenges, we propose the Interpolated Approximate Measurement (IAM), a framework natively designed for unlearning inference. IAM quantifies sample-level unlearning completeness by interpolating the model's generalization-fitting behavior gap on queried samples. IAM achieves strong performance in binary inclusion tests for exact unlearning and high correlation for approximate unlearning--scalable to LLMs using just one pre-trained shadow model. We theoretically analyze how IAM's scoring mechanism maintains performance efficiently. We then apply IAM to recent approximate unlearning algorithms, revealing general risks of both over-unlearning and under-unlearning, underscoring the need for stronger safeguards in approximate unlearning systems. The code is available at https://github.com/Happy2Git/Unlearning_Inference_IAM.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Machine unlearning, one of 12 directions in LLM safety and security. Builds on 58 of its 77 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

58 of 77 references.

Closest ancestors · 18

Same direction, newest first

  1. 2024MUSE: Machine Unlearning Six-Way Evaluation for Language Models8 Jul
  2. 2024Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning12 Mar
  3. 2024Guardrail Baselines for Unlearning in LLMs5 Mar
  4. 2024Rethinking Machine Unlearning for Large Language Models13 Feb
  5. 2023Large Language Model Unlearning14 Oct
  6. 2023In-Context Unlearning: Language Models as Few Shot Unlearners11 Oct
  7. 2023A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services15 Sep
  8. 2023Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening15 Aug
  9. 2023Model Sparsity Can Simplify Machine Unlearning11 Apr
  10. 2023Boundary Unlearning21 Mar
  11. 2022Forget Unlearning: Towards True Data-Deletion in Machine Learning17 Oct
  12. 2022Deep Unlearning via Randomized Conditionally Independent Hessians15 Apr
6 more
  1. 2021Fast Yet Effective Machine Unlearning17 Nov
  2. 2021On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning22 Oct
  3. 2021Unrolling SGD: Understanding Factors Influencing Machine Unlearning27 Sep
  4. 2021Machine Unlearning of Features and Labels26 Aug
  5. 2021Adaptive Machine Unlearning8 Jun
  6. 2021Machine Unlearning via Algorithmic Stability25 Feb

Other references · 40

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2020Extracting Training Data from Large Language Modelscited by 2,189
  5. 2017A Closer Look at Memorization in Deep Networkscited by 942
  6. 2021Membership Inference Attacks From First Principlescited by 878
  7. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  8. 2019Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networkscited by 618
  9. 2019Certified Data Removal from Machine Learning Modelscited by 535
  10. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 472
  11. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
  12. 2023Detecting Pretraining Data from Large Language Modelscited by 387
28 more
  1. 2020Approximate Data Deletion from Machine Learning Modelscited by 255
  2. 2020Influence Functions in Deep Learning Are Fragilecited by 252
  3. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 248
  4. 2020When Machine Unlearning Jeopardizes Privacycited by 198
  5. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 195
  6. 2020Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observationscited by 180
  7. 2020Mixed-Privacy Forgetting in Deep Networkscited by 163
  8. 2024A Tale of Tails: Model Collapse as a Change of Scaling Lawscited by 130
  9. 2024PLeak: Prompt Leaking Attacks against Large Language Model Applicationscited by 127
  10. 2023Low-Cost High-Power Membership Inference Attackscited by 107
  11. 2021Proof-of-Learning: Definitions and Practicecited by 84
  12. 2020Learn to Forget: Machine Unlearning via Neuron Maskingcited by 62
  13. 2024SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)cited by 59
  14. 2023LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Constructioncited by 58
  15. 2023Paloma: A Benchmark for Evaluating Language Model Fitcited by 54
  16. 2024CroissantLLM: A Truly Bilingual French-English Language Modelcited by 53
  17. 2022Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attackscited by 43
  18. 2023Inductive Graph Unlearningcited by 31
  19. 2023Tools for Verifying Neural Models' Training Datacited by 22
  20. 2024Communication Efficient and Provable Federated Unlearningcited by 18
  21. 2020Have you forgotten? A method to assess if machine learning models have forgotten datacited by 17
  22. 2025Short-length Adversarial Training Helps LLMs Defend Long-length Jailbreak Attacks: Theoretical and Empirical Evidencecited by 14
  23. 2023Preserving Node-level Privacy in Graph Neural Networkscited by 11
  24. 2024Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokenscited by 11
  25. 2025Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Runcited by 10
  26. 2021EMA: Auditing Data Removal from Trained Modelscited by 8
  27. 2024Revisiting Differentially Private Hyper-parameter Tuningcited by 5
  28. 2025FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attackscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 8.

11 papers.

  1. 2025Discrete Diffusion in Large Language and Multimodal Models: A Surveycited by 61
  2. 2025Towards Reliable Forgetting: A Survey on Machine Unlearning Verificationcited by 8
  3. 2026On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignmentcited by 8
  4. 2025Towards Reasoning-Preserving Unlearning in Multimodal Large Language Modelscited by 4
  5. 2026Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Modelscited by 2
  6. 2026CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Trainingcited by 2
  7. 2026SMI: Statistical Membership Inference for Reliable Unlearned Model Auditingcited by 1
  8. 2026ViMU: Benchmarking Video Metaphorical Understandingcited by 1
  9. 2026Auditing of Unlearning Algorithmscited by 1
  10. 2026FIT to Forget: Robust Continual Unlearning for Large Language Modelscited by 0
  11. 2026AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Explorationcited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2025Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
  3. 2026Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
  4. 2026Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
  5. 2024Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
  6. 2025Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks
  7. 2025Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack
  8. 2026Measuring the Depth of LLM Unlearning via Activation Patching