Model editing is where a model, such as a large language model, is “edited” to change the facts in the model. Machine unlearning is where a trained model is adjusted to “remove” one or more datapoints that were used to train the model, so that it behaves like a model that was trained without those datapoints.

See also Knowledge Editing.

• Tan et al 2023 - Massive Editing for Large Language Models via Meta Learning
• Zhang et al 2024 - Towards General Conceptual Model Editing via Adversarial Representation Engineering

• Nguyen et al 2022 - A Survey of Machine Unlearning
• Wang et al 2024 - Machine Unlearning: A Comprehensive Survey
• Paper lists
  • Awesome LLM Unlearning
• For NLP or LLMs
  • Geng et al 2025 - A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models

• Bourtoule et al 2019 - Machine Unlearning

• Eldan & Russinovich 2023 - Who’s Harry Potter? Approximate Unlearning in LLMs
• Yao et al 2023 - Large Language Model Unlearning
• Maini et al 2024 - TOFU: A Task of Fictitious Unlearning for LLMs
• Liu et al 2024 - Rethinking Machine Unlearning for Large Language Models
• Thaker et al 2024 - Guardrail Baselines for Unlearning in LLMs
• Gur-Arieh et al 2025 - Precise In-Parameter Concept Erasure in Large Language Models

• Guo et al 2024 - Certified Data Removal from Machine Learning Models

• Privacy
• Knowledge Editing