Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
Fuente:
arXiv
Saved in:
| Main Authors: | Hu, Zixuan, Shen, Li, Wang, Zhenyi, Wei, Yongxian, Tao, Dacheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Task-Distributionally Robust Data-Free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)
by: Hu, Zixuan, et al.
Published: (2023)
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
by: Hu, Zixuan, et al.
Published: (2024)
by: Hu, Zixuan, et al.
Published: (2024)
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
by: Hu, Zixuan, et al.
Published: (2025)
by: Hu, Zixuan, et al.
Published: (2025)
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)
by: Hu, Zixuan, et al.
Published: (2023)
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
by: Huang, Tiansheng, et al.
Published: (2024)
by: Huang, Tiansheng, et al.
Published: (2024)
Improving Large Language Models with Concept-Aware Fine-Tuning
by: Chen, Michael K., et al.
Published: (2025)
by: Chen, Michael K., et al.
Published: (2025)
Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
by: Liu, Guozhi, et al.
Published: (2025)
by: Liu, Guozhi, et al.
Published: (2025)
Learning to Learn from APIs: Black-Box Data-Free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)
by: Hu, Zixuan, et al.
Published: (2023)
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
by: Wei, Yongxian, et al.
Published: (2024)
by: Wei, Yongxian, et al.
Published: (2024)
FREE: Faster and Better Data-Free Meta-Learning
by: Wei, Yongxian, et al.
Published: (2024)
by: Wei, Yongxian, et al.
Published: (2024)
A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models
by: Chai, Yidong, et al.
Published: (2025)
by: Chai, Yidong, et al.
Published: (2025)
Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
by: Abdi, Hossein, et al.
Published: (2025)
by: Abdi, Hossein, et al.
Published: (2025)
Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
by: Li, Wenjuan, et al.
Published: (2026)
by: Li, Wenjuan, et al.
Published: (2026)
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
by: Huang, Kai, et al.
Published: (2023)
by: Huang, Kai, et al.
Published: (2023)
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
by: Kim, Gyuhak, et al.
Published: (2025)
by: Kim, Gyuhak, et al.
Published: (2025)
FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
by: Le, Khiem, et al.
Published: (2025)
by: Le, Khiem, et al.
Published: (2025)
Robust and Efficient Zeroth-Order LLM Fine-Tuning via Adaptive Bayesian Subspace Optimizer
by: Feng, Jian, et al.
Published: (2026)
by: Feng, Jian, et al.
Published: (2026)
Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
by: Hu, Shengchao, et al.
Published: (2024)
by: Hu, Shengchao, et al.
Published: (2024)
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
by: Wang, Jingtan, et al.
Published: (2024)
by: Wang, Jingtan, et al.
Published: (2024)
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
by: Guo, Wei, et al.
Published: (2025)
by: Guo, Wei, et al.
Published: (2025)
Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities
by: Hao, Zhiwei, et al.
Published: (2025)
by: Hao, Zhiwei, et al.
Published: (2025)
In-Training Defenses against Emergent Misalignment in Language Models
by: Kaczér, David, et al.
Published: (2025)
by: Kaczér, David, et al.
Published: (2025)
Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
by: Xu, Yichen, et al.
Published: (2026)
by: Xu, Yichen, et al.
Published: (2026)
Preserving Diversity in Supervised Fine-Tuning of Large Language Models
by: Li, Ziniu, et al.
Published: (2024)
by: Li, Ziniu, et al.
Published: (2024)
On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
by: Wang, Shumin, et al.
Published: (2026)
by: Wang, Shumin, et al.
Published: (2026)
SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
by: Chen, Shuhao, et al.
Published: (2026)
by: Chen, Shuhao, et al.
Published: (2026)
Efficient Inference Using Large Language Models with Limited Human Data: Fine-Tuning then Rectification
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retraining
by: Han, Bing, et al.
Published: (2025)
by: Han, Bing, et al.
Published: (2025)
Continual Task Learning through Adaptive Policy Self-Composition
by: Hu, Shengchao, et al.
Published: (2024)
by: Hu, Shengchao, et al.
Published: (2024)
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
by: Xiao, Jiancong, et al.
Published: (2025)
by: Xiao, Jiancong, et al.
Published: (2025)
Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
by: Hu, Senkang, et al.
Published: (2025)
by: Hu, Senkang, et al.
Published: (2025)
Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation
by: Huang, Tiansheng, et al.
Published: (2025)
by: Huang, Tiansheng, et al.
Published: (2025)
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
by: Wei, Quan, et al.
Published: (2025)
by: Wei, Quan, et al.
Published: (2025)
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
by: Zhang, Minmin, et al.
Published: (2026)
by: Zhang, Minmin, et al.
Published: (2026)
Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models
by: Ngo, Giang, et al.
Published: (2026)
by: Ngo, Giang, et al.
Published: (2026)
Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
by: Krishnan, Ranganath, et al.
Published: (2024)
by: Krishnan, Ranganath, et al.
Published: (2024)
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
by: Zhou, Jiajun, et al.
Published: (2025)
by: Zhou, Jiajun, et al.
Published: (2025)
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
by: Fang, Zihan, et al.
Published: (2024)
by: Fang, Zihan, et al.
Published: (2024)
Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
by: Balashov, Andrii
Published: (2025)
by: Balashov, Andrii
Published: (2025)
Similar Items
-
Task-Distributionally Robust Data-Free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023) -
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
by: Hu, Zixuan, et al.
Published: (2024) -
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
by: Hu, Zixuan, et al.
Published: (2025) -
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023) -
Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
by: Huang, Tiansheng, et al.
Published: (2024)