Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs

• URL: http://arxiv.org/abs/2507.04219v2
• Date: Fri, 11 Jul 2025 15:16:41 GMT
• Title: Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs
• Authors: Yan Scholten, Sophie Xhonneux, Leo Schwinn, Stephan Günnemann,
• Abstract summary: Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their training objectives.<n>We argue this not only risks reinforcing exposure to sensitive data, it also contradicts the principle of minimizing its use.<n>We propose a novel unlearning method - Partial Model Collapse (PMC), which does not require unlearning targets in the unlearning objective.
• Score: 44.8238758047607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their training objectives. We argue this not only risks reinforcing exposure to sensitive data, it also fundamentally contradicts the principle of minimizing its use. As a remedy, we propose a novel unlearning method - Partial Model Collapse (PMC), which does not require unlearning targets in the unlearning objective. Our approach is inspired by recent observations that training generative models on their own generations leads to distribution collapse, effectively removing information from the model. Our core idea is to leverage this collapse for unlearning by triggering collapse partially on the sensitive data. We theoretically analyze that our approach converges to the desired outcome, i.e. the LLM unlearns the information in the forget set. We empirically demonstrate that PMC overcomes two key limitations of existing unlearning approaches that explicitly optimize on unlearning targets, and more effectively removes private information from model outputs. Overall, our contributions represent an important step toward more comprehensive unlearning that aligns with real-world privacy constraints. Code available at https://www.cs.cit.tum.de/daml/partial-model-collapse/.

Related papers

• Align-then-Unlearn: Embedding Alignment for LLM Unlearning [41.94295877935867]
  Unlearning seeks to selectively remove specific data from trained models, such as personal information or copyrighted content.<n>We propose Align-then-Unlearn, a novel framework that performs unlearning in the semantic embedding space.
  arXiv  Detail & Related papers  (2025-06-16T07:48:01Z)
• Multi-Objective Large Language Model Unlearning [3.372396620898397]
  Gradient Ascent (GA) is a proactive way to decrease the prediction probability of the model on the target data.<n>We propose Multi-Objective Large Language Model Unlearning (MOLLM) algorithm to overcome gradient explosion and catastrophic forgetting.<n>Our empirical results verify that MoLLM outperforms the SOTA GA-based LLM unlearning methods in terms of unlearning effect and model utility preservation.
  arXiv  Detail & Related papers  (2024-12-29T09:35:56Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• LLM Unlearning via Loss Adjustment with Only Forget Data [20.310423152885217]
  We introduce Forget data only Loss AjustmenT (FLAT), a "flat" loss adjustment approach which addresses these issues. Empirical results demonstrate that our approach achieves superior unlearning performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-14T23:43:33Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.03511469562013]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• An Information Theoretic Approach to Machine Unlearning [43.423418819707784]
  To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important.<n>In this work, we address the zero-shot unlearning scenario, whereby an unlearning algorithm must be able to remove data given only a trained model and the data to be forgotten.<n>We derive a simple but principled zero-shot unlearning method based on the geometry of the model.
  arXiv  Detail & Related papers  (2024-02-02T13:33:30Z)
• Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
  Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
  arXiv  Detail & Related papers  (2023-10-31T03:35:59Z)
• Machine Unlearning of Features and Labels [72.81914952849334]
  We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
  arXiv  Detail & Related papers  (2021-08-26T04:42:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.