Imagen de Portada

Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wan, Yixin, Ramakrishna, Anil, Chang, Kai-Wei, Cevher, Volkan, Gupta, Rahul
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

SemEval-2025 Task 4: Unlearning sensitive content from Large Language Models
por: Ramakrishna, Anil, et al.
Publicado: (2025)

LUME: LLM Unlearning with Multitask Evaluations
por: Ramakrishna, Anil, et al.
Publicado: (2025)

Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
por: Bu, Zhiqi, et al.
Publicado: (2024)

Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models
por: Entesari, Taha, et al.
Publicado: (2025)

Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning
por: Takashiro, Shota, et al.
Publicado: (2024)

Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens
por: Koh, Seunghee, et al.
Publicado: (2026)

Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
por: Wang, Lingzhi, et al.
Publicado: (2024)

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning
por: Wu, Jiawei, et al.
Publicado: (2026)

To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
por: Tian, Bozhong, et al.
Publicado: (2024)

FIT to Forget: Robust Continual Unlearning for Large Language Models
por: Xu, Xiaoyu, et al.
Publicado: (2026)

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining
por: Bal, Melis Ilayda, et al.
Publicado: (2025)

Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification
por: Meng, Tao, et al.
Publicado: (2024)

UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
por: Patil, Vaidehi, et al.
Publicado: (2025)

BLUR: A Bi-Level Optimization Approach for LLM Unlearning
por: Reisizadeh, Hadi, et al.
Publicado: (2025)

Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
por: Chen, Haokun, et al.
Publicado: (2025)

Large Language Model Unlearning
por: Yao, Yuanshun, et al.
Publicado: (2023)

Model Unlearning Objectives Vary for Distinct Language Functions
por: Atil, Berk, et al.
Publicado: (2026)

Evaluating Deep Unlearning in Large Language Models
por: Wu, Ruihan, et al.
Publicado: (2024)

Unlearning Climate Misinformation in Large Language Models
por: Fore, Michael, et al.
Publicado: (2024)

A Survey on Unlearning in Large Language Models
por: Qiu, Ruichen, et al.
Publicado: (2025)

Machine Unlearning in Large Language Models
por: Gundavarapu, Saaketh Koundinya, et al.
Publicado: (2024)

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
por: Lin, Yujie, et al.
Publicado: (2026)

Forgetting to Forget: Attention Sink as A Gateway for Backdooring LLM Unlearning
por: Shang, Bingqi, et al.
Publicado: (2025)

RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models
por: Wang, Bichen, et al.
Publicado: (2024)

Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond
por: Fan, Chongyu, et al.
Publicado: (2025)

Investigating Model Editing for Unlearning in Large Language Models
por: Hossain, Shariqah, et al.
Publicado: (2025)

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
por: Xie, Wanyun, et al.
Publicado: (2025)

LLM Unlearning via Loss Adjustment with Only Forget Data
por: Wang, Yaxuan, et al.
Publicado: (2024)

Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
por: Tang, Haoyu, et al.
Publicado: (2024)

Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models
por: Choi, Minseok, et al.
Publicado: (2024)

Offset Unlearning for Large Language Models
por: Huang, James Y., et al.
Publicado: (2024)

Understanding the Dilemma of Unlearning for Large Language Models
por: Zhang, Qingjie, et al.
Publicado: (2025)

Rethinking Machine Unlearning for Large Language Models
por: Liu, Sijia, et al.
Publicado: (2024)

Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning
por: Lee, Hong kyu, et al.
Publicado: (2025)

Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs
por: P, Dinesh Srivasthav, et al.
Publicado: (2026)

UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
por: Wang, Wenyu, et al.
Publicado: (2025)

OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
por: Zheng, Hao, et al.
Publicado: (2025)

Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training
por: Tran, Toan, et al.
Publicado: (2025)

White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
por: Wan, Yixin, et al.
Publicado: (2024)

SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
por: Xu, Tianyang, et al.
Publicado: (2025)