Imagen de Portada

Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Dou, Guangyao
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Avoiding Copyright Infringement via Large Language Model Unlearning
por: Dou, Guangyao, et al.
Publicado: (2024)

Certified Mitigation of Worst-Case LLM Copyright Infringement
por: Zhang, Jingyu, et al.
Publicado: (2025)

SCOPE: Intrinsic Semantic Space Control for Mitigating Copyright Infringement in LLMs
por: Zhang, Zhenliang, et al.
Publicado: (2025)

Towards Safer Large Language Models through Machine Unlearning
por: Liu, Zheyuan, et al.
Publicado: (2024)

Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models
por: Liu, Zheyuan, et al.
Publicado: (2025)

Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
por: Xu, Qipan, et al.
Publicado: (2025)

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

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

Mitigating Biases in Language Models via Bias Unlearning
por: Liu, Dianqing, et al.
Publicado: (2025)

Machine Unlearning in Generative AI: A Survey
por: Liu, Zheyuan, et al.
Publicado: (2024)

A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models
por: Ma, Rui, et al.
Publicado: (2024)

Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport
por: Choi, Minseok, et al.
Publicado: (2024)

Copyright Traps for Large Language Models
por: Meeus, Matthieu, et al.
Publicado: (2024)

Self-ensemble: Mitigating Confidence Mis-calibration for Large Language Models
por: Xu, Zicheng, et al.
Publicado: (2025)

Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
por: Liu, Zheyuan, et al.
Publicado: (2024)

Mitigating Social Biases in Language Models through Unlearning
por: Dige, Omkar, et al.
Publicado: (2024)

Steering Multimodal Large Language Models Decoding for Context-Aware Safety
por: Liu, Zheyuan, et al.
Publicado: (2025)

CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
por: Liu, Shunchang, et al.
Publicado: (2025)

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
por: Xing, Shangyu, et al.
Publicado: (2024)

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)

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)

Evaluating the Feasibility and Accuracy of Large Language Models for Medical History-Taking in Obstetrics and Gynecology
por: Liu, Dou, et al.
Publicado: (2025)

Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
por: Lu, Weikai, et al.
Publicado: (2024)

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

The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
por: de la Rosa, Javier, et al.
Publicado: (2024)

Randomization Techniques to Mitigate the Risk of Copyright Infringement
por: Chen, Wei-Ning, et al.
Publicado: (2024)

SoK: Large Language Model Copyright Auditing via Fingerprinting
por: Shao, Shuo, et al.
Publicado: (2025)

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

GRAIL: Gradient-Based Adaptive Unlearning for Privacy and Copyright in LLMs
por: Kim, Kun-Woo, et al.
Publicado: (2025)

Leave No TRACE: Black-box Detection of Copyrighted Dataset Usage in Large Language Models via Watermarking
por: Zhang, Jingqi, et al.
Publicado: (2025)

Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?
por: Xu, Naen, et al.
Publicado: (2025)

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

Copyright Infringement Detection in Text-to-Image Diffusion Models via Differential Privacy
por: Man, Xiafeng, et al.
Publicado: (2025)

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

Measuring Copyright Risks of Large Language Model via Partial Information Probing
por: Zhao, Weijie, et al.
Publicado: (2024)

Large Language Model Unlearning via Embedding-Corrupted Prompts
por: Liu, Chris Yuhao, et al.
Publicado: (2024)

Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning
por: Wan, Yixin, et al.
Publicado: (2025)

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