Cover Image

Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Jingyang, Sun, Jingwei, Yeats, Eric, Ouyang, Yang, Kuo, Martin, Zhang, Jianyi, Yang, Hao Frank, Li, Hai
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility
by: Kuo, Martin, et al.
Published: (2025)

Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
by: Liu, Yudong, et al.
Published: (2025)

SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval
by: Lin, Yueqian, et al.
Published: (2024)

DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration
by: Kuo, Martin, et al.
Published: (2023)

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
by: Zhang, Jianyi, et al.
Published: (2024)

Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data
by: Guo, Xu, et al.
Published: (2026)

Exploring Forgetting in Large Language Model Pre-Training
by: Liao, Chonghua, et al.
Published: (2024)

Data Proportion Detection for Optimized Data Management for Large Language Models
by: Liang, Hao, et al.
Published: (2024)

OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration
by: Wang, Shaobo, et al.
Published: (2026)

Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
by: Tian, Junfeng, et al.
Published: (2024)

Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
by: Zhuang, Yuchen, et al.
Published: (2025)

Regurgitative Training: The Value of Real Data in Training Large Language Models
by: Zhang, Jinghui, et al.
Published: (2024)

Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis
by: Boughorbel, Sabri, et al.
Published: (2024)

A Survey on Efficient Large Language Model Training: From Data-centric Perspectives
by: Luo, Junyu, et al.
Published: (2025)

From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
by: Zhang, Ruiqi, et al.
Published: (2026)

Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders
by: Xin, Yuan, et al.
Published: (2024)

Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!
by: Wildenburg, Frank, et al.
Published: (2024)

Detecting Training Data of Large Language Models via Expectation Maximization
by: Kim, Gyuwan, et al.
Published: (2024)

H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
by: Kuo, Martin, et al.
Published: (2025)

Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens
by: Zhang, Anqi, et al.
Published: (2024)

"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
by: Weller, Orion, et al.
Published: (2023)

Large Language Models are Demonstration Pre-Selectors for Themselves
by: Jin, Jiarui, et al.
Published: (2025)

BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
by: Dong, Guosheng, et al.
Published: (2024)

SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models
by: Zhang, Jianyi, et al.
Published: (2024)

Boosting Disfluency Detection with Large Language Model as Disfluency Generator
by: Cheng, Zhenrong, et al.
Published: (2024)

MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
by: Chen, Qian, et al.
Published: (2025)

Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
by: Yang, Kang, et al.
Published: (2025)

DataMan: Data Manager for Pre-training Large Language Models
by: Peng, Ru, et al.
Published: (2025)

Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data
by: Zhang, Jingyu, et al.
Published: (2024)

Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models
by: Ma, Shengjie, et al.
Published: (2025)

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
by: Zhu, Zhaowei, et al.
Published: (2023)

On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
by: Zhang, Charlie, et al.
Published: (2025)

Cross-model Control: Improving Multiple Large Language Models in One-time Training
by: Wu, Jiayi, et al.
Published: (2024)

Measuring the Impact of Lexical Training Data Coverage on Hallucination Detection in Large Language Models
by: Zhang, Shuo, et al.
Published: (2025)

Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper
by: Ishihara, Shotaro, et al.
Published: (2024)

PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training
by: Cheng, Yuhan, et al.
Published: (2026)

Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data
by: Kondo, Minato, et al.
Published: (2024)

Sparse is Enough in Fine-tuning Pre-trained Large Language Models
by: Song, Weixi, et al.
Published: (2023)

Detecting RLVR Training Data via Structural Convergence of Reasoning
by: Zhang, Hongbo, et al.
Published: (2026)

MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
by: Wang, Zhongxi, et al.
Published: (2026)