Detecting Pretraining Data from Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Shi, Weijia, Ajith, Anirudh, Xia, Mengzhou, Huang, Yangsibo, Liu, Daogao, Blevins, Terra, Chen, Danqi, Zettlemoyer, Luke |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method
by: Zhang, Weichao, et al.
Published: (2024)
by: Zhang, Weichao, et al.
Published: (2024)
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack
by: Antebi, Sagiv, et al.
Published: (2025)
by: Antebi, Sagiv, et al.
Published: (2025)
What is in Your Safe Data? Identifying Benign Data that Breaks Safety
by: He, Luxi, et al.
Published: (2024)
by: He, Luxi, et al.
Published: (2024)
Downstream Trade-offs of a Family of Text Watermarks
by: Ajith, Anirudh, et al.
Published: (2023)
by: Ajith, Anirudh, et al.
Published: (2023)
Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy
by: Huang, Yangsibo, et al.
Published: (2024)
by: Huang, Yangsibo, et al.
Published: (2024)
Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs
by: Liu, Honghao, et al.
Published: (2026)
by: Liu, Honghao, et al.
Published: (2026)
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks
by: Li, Ziqiang, et al.
Published: (2024)
by: Li, Ziqiang, et al.
Published: (2024)
Comparing Hallucination Detection Metrics for Multilingual Generation
by: Kang, Haoqiang, et al.
Published: (2024)
by: Kang, Haoqiang, et al.
Published: (2024)
Yet Another Watermark for Large Language Models
by: Bao, Siyuan, et al.
Published: (2025)
by: Bao, Siyuan, et al.
Published: (2025)
S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models
by: Yuan, Xiaohan, et al.
Published: (2024)
by: Yuan, Xiaohan, et al.
Published: (2024)
Efficient Detection of Toxic Prompts in Large Language Models
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
Denial-of-Service Poisoning Attacks against Large Language Models
by: Gao, Kuofeng, et al.
Published: (2024)
by: Gao, Kuofeng, et al.
Published: (2024)
ConfGuard: A Simple and Effective Backdoor Detection for Large Language Models
by: Wang, Zihan, et al.
Published: (2025)
by: Wang, Zihan, et al.
Published: (2025)
Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
by: Yi, Biao, et al.
Published: (2025)
by: Yi, Biao, et al.
Published: (2025)
Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models
by: Li, Haoran, et al.
Published: (2024)
by: Li, Haoran, et al.
Published: (2024)
Multi-Agent Collaboration in Incident Response with Large Language Models
by: Liu, Zefang
Published: (2024)
by: Liu, Zefang
Published: (2024)
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
by: Zheng, JiaYing, et al.
Published: (2024)
by: Zheng, JiaYing, et al.
Published: (2024)
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
by: Li, Haoran, et al.
Published: (2024)
by: Li, Haoran, et al.
Published: (2024)
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning
by: Yi, Biao, et al.
Published: (2025)
by: Yi, Biao, et al.
Published: (2025)
Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens
by: Zhang, Anqi, et al.
Published: (2024)
by: Zhang, Anqi, et al.
Published: (2024)
Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models
by: He, Yu, et al.
Published: (2025)
by: He, Yu, et al.
Published: (2025)
How Susceptible are Large Language Models to Ideological Manipulation?
by: Chen, Kai, et al.
Published: (2024)
by: Chen, Kai, et al.
Published: (2024)
Copyright Traps for Large Language Models
by: Meeus, Matthieu, et al.
Published: (2024)
by: Meeus, Matthieu, et al.
Published: (2024)
Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models
by: Kadali, Sri Durga Sai Sowmya, et al.
Published: (2026)
by: Kadali, Sri Durga Sai Sowmya, et al.
Published: (2026)
Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs
by: Sekar, Anirudh, et al.
Published: (2026)
by: Sekar, Anirudh, et al.
Published: (2026)
EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models
by: Wu, Jialin, et al.
Published: (2025)
by: Wu, Jialin, et al.
Published: (2025)
Resource Consumption Red-Teaming for Large Vision-Language Models
by: Gao, Haoran, et al.
Published: (2025)
by: Gao, Haoran, et al.
Published: (2025)
Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services
by: Li, Yansong, et al.
Published: (2023)
by: Li, Yansong, et al.
Published: (2023)
Data Defenses Against Large Language Models
by: Agnew, William, et al.
Published: (2024)
by: Agnew, William, et al.
Published: (2024)
Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models
by: Jiang, Peihai, et al.
Published: (2025)
by: Jiang, Peihai, et al.
Published: (2025)
DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack
by: Li, Hao, et al.
Published: (2025)
by: Li, Hao, et al.
Published: (2025)
Privacy in Large Language Models: Attacks, Defenses and Future Directions
by: Li, Haoran, et al.
Published: (2023)
by: Li, Haoran, et al.
Published: (2023)
SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models
by: Afane, Mohamed, et al.
Published: (2025)
by: Afane, Mohamed, et al.
Published: (2025)
Understanding and Mitigating Over-refusal for Large Language Models via Safety Representation
by: Zhang, Junbo, et al.
Published: (2025)
by: Zhang, Junbo, et al.
Published: (2025)
Retrieval-Augmented Defense: Adaptive and Controllable Jailbreak Prevention for Large Language Models
by: Yang, Guangyu, et al.
Published: (2025)
by: Yang, Guangyu, et al.
Published: (2025)
Rethinking Backdoor Detection Evaluation for Language Models
by: Yan, Jun, et al.
Published: (2024)
by: Yan, Jun, et al.
Published: (2024)
Large Language Models as Carriers of Hidden Messages
by: Hoscilowicz, Jakub, et al.
Published: (2024)
by: Hoscilowicz, Jakub, et al.
Published: (2024)
Token-Level Privacy in Large Language Models
by: Harel, Re'em, et al.
Published: (2025)
by: Harel, Re'em, et al.
Published: (2025)
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
by: Li, Haodong, et al.
Published: (2024)
by: Li, Haodong, et al.
Published: (2024)
Similar Items
-
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning
by: Chua, Lynn, et al.
Published: (2024) -
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method
by: Zhang, Weichao, et al.
Published: (2024) -
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack
by: Antebi, Sagiv, et al.
Published: (2025) -
What is in Your Safe Data? Identifying Benign Data that Breaks Safety
by: He, Luxi, et al.
Published: (2024) -
Downstream Trade-offs of a Family of Text Watermarks
by: Ajith, Anirudh, et al.
Published: (2023)