Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Unlearning in LLMs: Methods, Evaluation, and Open Challenges

Tyler Lizzo, Larry Heck

TL;DR

This paper addresses the challenge of selectively removing or suppressing learned information in large language models (LLMs) without full retraining, motivated by privacy, copyright, safety, and fairness concerns. It surveys a broad taxonomy of unlearning methods—data-centric, parameter-centric, architecture-centric, hybrid, and other approaches—and surveys the evaluation ecosystem, including benchmarks like TOFU and BLUR, as well as diverse datasets spanning synthetic, general, cross-language, and multimodal settings. The work highlights key approaches (e.g., optimization-based forgetting, synthetic data replacement, knowledge distillation, auxiliary modules, contrastive decoding, LoRA-level edits, and subspace methods) and discusses their trade-offs in forgetting guarantees, efficiency, and utility preservation, culminating in a roadmap of open problems. The authors emphasize the need for formal forgetting guarantees, scalable solutions for trillion-parameter models, cross-lingual and multimodal unlearning, governance frameworks, and robustness against adversarial relearning, underscoring the practical significance of reliable, responsible unlearning for deployment at scale.

Abstract

Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, copyright, security, and bias. Machine unlearning has emerged as a promising paradigm for selectively removing knowledge or data from trained models without full retraining. In this survey, we provide a structured overview of unlearning methods for LLMs, categorizing existing approaches into data-centric, parameter-centric, architecture-centric, hybrid, and other strategies. We also review the evaluation ecosystem, including benchmarks, metrics, and datasets designed to measure forgetting effectiveness, knowledge retention, and robustness. Finally, we outline key challenges and open problems, such as scalable efficiency, formal guarantees, cross-language and multimodal unlearning, and robustness against adversarial relearning. By synthesizing current progress and highlighting open directions, this paper aims to serve as a roadmap for developing reliable and responsible unlearning techniques in large language models.

Unlearning in LLMs: Methods, Evaluation, and Open Challenges

TL;DR

This paper addresses the challenge of selectively removing or suppressing learned information in large language models (LLMs) without full retraining, motivated by privacy, copyright, safety, and fairness concerns. It surveys a broad taxonomy of unlearning methods—data-centric, parameter-centric, architecture-centric, hybrid, and other approaches—and surveys the evaluation ecosystem, including benchmarks like TOFU and BLUR, as well as diverse datasets spanning synthetic, general, cross-language, and multimodal settings. The work highlights key approaches (e.g., optimization-based forgetting, synthetic data replacement, knowledge distillation, auxiliary modules, contrastive decoding, LoRA-level edits, and subspace methods) and discusses their trade-offs in forgetting guarantees, efficiency, and utility preservation, culminating in a roadmap of open problems. The authors emphasize the need for formal forgetting guarantees, scalable solutions for trillion-parameter models, cross-lingual and multimodal unlearning, governance frameworks, and robustness against adversarial relearning, underscoring the practical significance of reliable, responsible unlearning for deployment at scale.

Abstract

Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, copyright, security, and bias. Machine unlearning has emerged as a promising paradigm for selectively removing knowledge or data from trained models without full retraining. In this survey, we provide a structured overview of unlearning methods for LLMs, categorizing existing approaches into data-centric, parameter-centric, architecture-centric, hybrid, and other strategies. We also review the evaluation ecosystem, including benchmarks, metrics, and datasets designed to measure forgetting effectiveness, knowledge retention, and robustness. Finally, we outline key challenges and open problems, such as scalable efficiency, formal guarantees, cross-language and multimodal unlearning, and robustness against adversarial relearning. By synthesizing current progress and highlighting open directions, this paper aims to serve as a roadmap for developing reliable and responsible unlearning techniques in large language models.
Paper Structure (39 sections, 4 equations, 11 figures)

This paper contains 39 sections, 4 equations, 11 figures.

Table of Contents

  1. Introduction
  2. History of Unlearning
  3. Taxonomy of Approaches
  4. Data-Centric Unlearning
  5. Optimization-Based Unlearning.
  6. Synthetic Data Replacement.
  7. Knowledge Distillation.
  8. Architecture-Centric Unlearning
  9. Auxiliary Models.
  10. Contrastive Decoding.
  11. Federated Unlearning.
  12. Parameter-Centric Unlearning
  13. Direct Fine-Tuning.
  14. Task Vectors.
  15. Subspace Projection.
  16. ...and 24 more sections

Figures (11)

  • Figure 1: Taxonomy of LLM Unlearning Methods and Evaluation Techniques
  • Figure 2: The teacher-student paradigm proposed in E2URec wang2024efficienteffectiveunlearninglarge, showing both the construction of the teachers and how the student model uses those teachers.
  • Figure 3: Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC) mitchell2022memorybasedmodeleditingscale
  • Figure 4: The Oblivionis Federated Unlearning Pipeline
  • Figure 5: The Task Vector method ilharco2023editing
  • ...and 6 more figures