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--- ml:model_editing_and_unlearning [2025/05/16 05:06] – [Related Pages] jmflanig
+++ ml:model_editing_and_unlearning [2025/07/07 07:13] (current) – [In NLP or LLMs] jmflanig
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 ====== Model Editing and Unlearning ======
-//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.
+//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.  The datapoints to remove can either be specific datapoints from the training set, or classes of datapoints, such as all datapoints about bioweapons.
 ===== Model Editing =====
 ==== In NLP ====
+See also [[nlp:Knowledge Editing]].
   * [[https://arxiv.org/pdf/2311.04661.pdf|Tan et al 2023 - Massive Editing for Large Language Models via Meta Learning]]
   * [[https://arxiv.org/pdf/2404.13752|Zhang et al 2024 - Towards General Conceptual Model Editing via Adversarial Representation Engineering]]
@@ Line 12: / Line 13: @@
 ==== Overviews ====
   * [[https://arxiv.org/pdf/2209.02299|Nguyen et al 2022 - A Survey of Machine Unlearning]]
-  * [[https://github.com/chrisliu298/awesome-llm-unlearning|Awesome LLM Unlearning]]
+  * [[https://arxiv.org/pdf/2306.03558|Xu et al 2023 - Machine Unlearning: A Survey]]
+  * [[https://arxiv.org/pdf/2405.07406|Wang et al 2024 - Machine Unlearning: A Comprehensive Survey]]
+  * [[https://arxiv.org/pdf/2407.20516|Liu et al 2024 - Machine Unlearning in Generative AI: A Survey]]
+  * **Paper lists**
+    * [[https://github.com/chrisliu298/awesome-llm-unlearning|Awesome LLM Unlearning]]
+  * ** For NLP or LLMs **
+    * [[https://arxiv.org/pdf/2402.08787.pdf|Liu et al 2024 - Rethinking Machine Unlearning for Large Language Models]] (This is also a survey paper.)
+    * [[https://arxiv.org/pdf/2503.01854|Geng et al 2025 - A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models]]
 ==== Key Papers ====
   * [[https://browse.arxiv.org/pdf/1912.03817.pdf|Bourtoule et al 2019 - Machine Unlearning]]
-==== In NLP ====
+==== In NLP or LLMs ====
   * [[https://arxiv.org/pdf/2310.02238.pdf|Eldan & Russinovich 2023 - Who’s Harry Potter? Approximate Unlearning in LLMs]]
   * [[https://arxiv.org/pdf/2310.10683.pdf|Yao et al 2023 - Large Language Model Unlearning]]
@@ Line 23: / Line 31: @@
   * [[https://arxiv.org/pdf/2402.08787.pdf|Liu et al 2024 - Rethinking Machine Unlearning for Large Language Models]]
   * [[https://arxiv.org/pdf/2403.03329.pdf|Thaker et al 2024 - Guardrail Baselines for Unlearning in LLMs]]
+  * [[https://arxiv.org/pdf/2410.02760|Gandikota et al 2024 - Erasing Conceptual Knowledge from Language Models]]
+  * [[https://arxiv.org/pdf/2505.22586|Gur-Arieh et al 2025 - Precise In-Parameter Concept Erasure in Large Language Models]]
 ==== Theory Papers ====