{"total":74,"rows":[{"id":"2406.04244","title":"Benchmark Data Contamination of Large Language Models: A Survey","created":"2024-06-06","cited_by_count":147,"area":null},{"id":"2504.15585","title":"A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment","created":"2025-04-22","cited_by_count":116,"area":"lr"},{"id":"2404.02936","title":"Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models","created":"2024-04-03","cited_by_count":103,"area":null},{"id":"2406.16201","title":"Blind Baselines Beat Membership Inference Attacks for Foundation Models","created":"2024-06-23","cited_by_count":69,"area":"safety"},{"id":"2406.15968","title":"ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods","created":"2024-06-23","cited_by_count":64,"area":"safety"},{"id":"2410.22108","title":"Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench","created":"2024-10-29","cited_by_count":64,"area":"mllm"},{"id":"2406.17975","title":"SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)","created":"2024-06-25","cited_by_count":59,"area":"safety"},{"id":"2409.14781","title":"Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method","created":"2024-09-23","cited_by_count":55,"area":null},{"id":"2409.19798","title":"Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data","created":"2024-09-29","cited_by_count":50,"area":"safety"},{"id":"2502.17521","title":"Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation","created":"2025-02-23","cited_by_count":38,"area":null},{"id":"2502.14425","title":"A Survey on Data Contamination for Large Language Models","created":"2025-02-20","cited_by_count":35,"area":null},{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2406.14644","title":"Unveiling the Spectrum of Data Contamination in Language Models: A Survey from Detection to Remediation","created":"2024-06-20","cited_by_count":30,"area":null},{"id":"2410.18966","title":"Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions","created":"2024-10-24","cited_by_count":26,"area":null},{"id":"2502.15910","title":"Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models","created":"2025-02-21","cited_by_count":26,"area":"mllm"},{"id":"2406.10952","title":"Avoiding Copyright Infringement via Large Language Model Unlearning","created":"2024-06-16","cited_by_count":24,"area":"safety"},{"id":"2404.11262","title":"Sampling-based Pseudo-Likelihood for Membership Inference Attacks","created":"2024-04-17","cited_by_count":20,"area":"safety"},{"id":"2409.03363","title":"Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding","created":"2024-09-05","cited_by_count":20,"area":null},{"id":"2407.17417","title":"Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?","created":"2024-07-24","cited_by_count":18,"area":"safety"},{"id":"2502.00678","title":"How Contaminated Is Your Benchmark? Quantifying Dataset Leakage in Large Language Models with Kernel Divergence","created":"2025-02-02","cited_by_count":15,"area":null},{"id":"2410.07582","title":"Detecting Training Data of Large Language Models via Expectation Maximization","created":"2024-10-10","cited_by_count":13,"area":"safety"},{"id":"2412.05734","title":"LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage","created":"2024-12-07","cited_by_count":13,"area":"agents"},{"id":"2405.15523","title":"The Mosaic Memory of Large Language Models","created":"2024-05-24","cited_by_count":10,"area":null},{"id":"2502.15680","title":"Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training","created":"2025-02-21","cited_by_count":10,"area":null},{"id":"2506.10424","title":"SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks","created":"2025-06-12","cited_by_count":10,"area":"safety"},{"id":"2507.11405","title":"DCR: Quantifying Data Contamination in LLMs Evaluation","created":"2025-07-15","cited_by_count":10,"area":null},{"id":"2407.02596","title":"Towards More Realistic Extraction Attacks: An Adversarial Perspective","created":"2024-07-02","cited_by_count":8,"area":null},{"id":"2410.10880","title":"Fine-tuning can Help Detect Pretraining Data from Large Language Models","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2503.12072","title":"Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models","created":"2025-03-15","cited_by_count":8,"area":null},{"id":"2410.17218","title":"Creativity in AI: Progresses and Challenges","created":"2024-10-22","cited_by_count":7,"area":null},{"id":"2506.15271","title":"Unlocking Post-hoc Dataset Inference with Synthetic Data","created":"2025-06-18","cited_by_count":7,"area":"synth"},{"id":"2508.09603","title":"The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage","created":"2025-08-13","cited_by_count":7,"area":"safety"},{"id":"2411.04535","title":"Meta-Reasoning Improves Tool Use in Large Language Models","created":"2024-11-07","cited_by_count":6,"area":"lr"},{"id":"2506.17353","title":"Differentiation-Based Extraction of Proprietary Data from Fine-Tuned LLMs","created":"2025-06-20","cited_by_count":6,"area":"alignment"},{"id":"2509.14278","title":"Beyond Data Privacy: New Privacy Risks for Large Language Models","created":"2025-09-16","cited_by_count":6,"area":null},{"id":"2510.02962","title":"Leave No TRACE: Black-box Detection of Copyrighted Dataset Usage in Large Language Models via Watermarking","created":"2025-10-03","cited_by_count":5,"area":"safety"},{"id":"2408.16073","title":"Using Large Language Models to Create AI Personas for Replication, Generalization and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings","created":"2024-08-28","cited_by_count":4,"area":null},{"id":"2503.06792","title":"On the Mutual Influence of Gender and Occupation in LLM Representations","created":"2025-03-09","cited_by_count":4,"area":null},{"id":"2506.17265","title":"SUA: Stealthy Multimodal Large Language Model Unlearning Attack","created":"2025-06-10","cited_by_count":4,"area":"mllm"},{"id":"2510.09655","title":"Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique","created":"2025-10-07","cited_by_count":4,"area":null},{"id":"2505.00020","title":"Beyond Public Access in LLM Pre-Training Data","created":"2025-04-24","cited_by_count":3,"area":"safety"},{"id":"2510.05699","title":"Membership Inference Attacks on Tokenizers of Large Language Models","created":"2025-10-07","cited_by_count":3,"area":"safety"},{"id":"2512.08946","title":"Large Language Models as Search Engines: Societal Challenges","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2601.11314","title":"Membership Inference on LLMs in the Wild","created":"2026-01-16","cited_by_count":3,"area":"safety"},{"id":"2404.17143","title":"Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2405.08134","title":"Many-Shot Regurgitation (MSR) Prompting","created":"2024-05-13","cited_by_count":2,"area":"safety"},{"id":"2412.04947","title":"C$^2$LEVA: Toward Comprehensive and Contamination-Free Language Model Evaluation","created":"2024-12-06","cited_by_count":2,"area":null},{"id":"2412.08549","title":"Watermarking Training Data of Music Generation Models","created":"2024-12-11","cited_by_count":2,"area":"safety"},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2505.03392","title":"Automatic Calibration for Membership Inference Attack on Large Language Models","created":"2025-05-06","cited_by_count":2,"area":"safety"},{"id":"2509.20909","title":"LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories","created":"2025-09-25","cited_by_count":2,"area":null},{"id":"2601.13607","title":"When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models","created":"2026-01-20","cited_by_count":2,"area":"safety"},{"id":"2410.04454","title":"Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture","created":"2024-10-06","cited_by_count":1,"area":null},{"id":"2502.17358","title":"DIS-CO: Discovering Copyrighted Content in VLMs Training Data","created":"2025-02-24","cited_by_count":1,"area":"mllm"},{"id":"2505.11837","title":"On Membership Inference Attacks in Knowledge Distillation","created":"2025-05-17","cited_by_count":1,"area":"safety"},{"id":"2510.09717","title":"Provable Training Data Identification for Large Language Models","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2510.20926","title":"FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2511.11914","title":"Forgetting-MarI: LLM Unlearning via Marginal Information Regularization","created":"2025-11-14","cited_by_count":1,"area":"safety"},{"id":"2601.19936","title":"Gap-K%: Measuring Top-1 Prediction Gap for Detecting Pretraining Data","created":"2026-01-16","cited_by_count":1,"area":null},{"id":"2602.05252","title":"Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks","created":"2026-02-05","cited_by_count":1,"area":"safety"},{"id":"2509.20088","title":"Causal Understanding by LLMs: The Role of Uncertainty","created":"2025-09-24","cited_by_count":0,"area":null},{"id":"2510.25941","title":"RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline","created":"2025-10-29","cited_by_count":0,"area":"agents"},{"id":"2511.00192","title":"EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs","created":"2025-10-31","cited_by_count":0,"area":"safety"},{"id":"2511.20623","title":"Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.06827","title":"PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection","created":"2026-01-11","cited_by_count":0,"area":null},{"id":"2601.12937","title":"On the Evidentiary Limits of Membership Inference for Copyright Auditing","created":"2026-01-19","cited_by_count":0,"area":"safety"},{"id":"2602.19020","title":"Learning to Detect Language Model Training Data via Active Reconstruction","created":"2026-02-22","cited_by_count":0,"area":"safety"},{"id":"2603.04828","title":"From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2604.19561","title":"Detecting Data Contamination in Large Language Models","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2605.21543","title":"Provable Joint Decontamination for Benchmarking Multiple Large Language Models","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2608.10462","title":"Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2609.10611","title":"Black-Box Membership Inference via Word-Level Probability Estimation","created":"2026-09-08","cited_by_count":0,"area":"safety"},{"id":"2609.14649","title":"CIG-MIA: Context-Induced Information Gain Membership Inference Attacks against Retrieval-Augmented Generation","created":"2026-09-13","cited_by_count":0,"area":"rag"},{"id":"2610.17332","title":"COMPASS: Membership Inference Attack Against Vision Large Language Models via Adaptive Noise Calibration","created":"2026-10-12","cited_by_count":0,"area":"safety"}],"next_cursor":null}