A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909630124261376 |
|---|---|
| author | Geng, Jiahui Li, Qing Woisetschlaeger, Herbert Chen, Zongxiong Cai, Fengyu Wang, Yuxia Nakov, Preslav Jacobsen, Hans-Arno Karray, Fakhri |
| author_facet | Geng, Jiahui Li, Qing Woisetschlaeger, Herbert Chen, Zongxiong Cai, Fengyu Wang, Yuxia Nakov, Preslav Jacobsen, Hans-Arno Karray, Fakhri |
| contents | This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a principled approach to removing the influence of undesirable data (e.g., sensitive or illegal information) from LLMs, while preserving their overall utility without requiring full retraining. Despite growing research interest, there is no comprehensive survey that systematically organizes existing work and distills key insights; here, we aim to bridge this gap. We begin by introducing the definition and the paradigms of LLM unlearning, followed by a comprehensive taxonomy of existing unlearning studies. Next, we categorize current unlearning approaches, summarizing their strengths and limitations. Additionally, we review evaluation metrics and benchmarks, providing a structured overview of current assessment methodologies. Finally, we outline promising directions for future research, highlighting key challenges and opportunities in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models Geng, Jiahui Li, Qing Woisetschlaeger, Herbert Chen, Zongxiong Cai, Fengyu Wang, Yuxia Nakov, Preslav Jacobsen, Hans-Arno Karray, Fakhri Computation and Language Artificial Intelligence This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a principled approach to removing the influence of undesirable data (e.g., sensitive or illegal information) from LLMs, while preserving their overall utility without requiring full retraining. Despite growing research interest, there is no comprehensive survey that systematically organizes existing work and distills key insights; here, we aim to bridge this gap. We begin by introducing the definition and the paradigms of LLM unlearning, followed by a comprehensive taxonomy of existing unlearning studies. Next, we categorize current unlearning approaches, summarizing their strengths and limitations. Additionally, we review evaluation metrics and benchmarks, providing a structured overview of current assessment methodologies. Finally, we outline promising directions for future research, highlighting key challenges and opportunities in the field. |
| title | A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01854 |