{"total":19,"rows":[{"id":"2406.18664","title":"Evaluating Copyright Takedown Methods for Language Models","created":"2024-06-26","cited_by_count":43,"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":"2407.15100","title":"A General Framework for Data-Use Auditing of ML Models","created":"2024-07-21","cited_by_count":16,"area":null},{"id":"2405.15523","title":"The Mosaic Memory of Large Language Models","created":"2024-05-24","cited_by_count":10,"area":null},{"id":"2504.13416","title":"STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings","created":"2025-04-18","cited_by_count":8,"area":null},{"id":"2507.05622","title":"DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective","created":"2025-07-08","cited_by_count":8,"area":null},{"id":"2408.11546","title":"Memorization in In-Context Learning","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2407.04981","title":"TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs","created":"2024-07-06","cited_by_count":4,"area":null},{"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":"2512.16059","title":"ContextLeak: Auditing Leakage in Private In-Context Learning Methods","created":"2025-12-18","cited_by_count":4,"area":null},{"id":"2406.05900","title":"Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research","created":"2024-06-09","cited_by_count":3,"area":null},{"id":"2512.08946","title":"Large Language Models as Search Engines: Societal Challenges","created":"2025-11-24","cited_by_count":3,"area":null},{"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":"2502.16290","title":"Interrogating LLM design under a fair learning doctrine","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2507.19419","title":"TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability","created":"2025-07-25","cited_by_count":0,"area":null},{"id":"2512.17075","title":"Perturb Your Data: Paraphrase-Guided Training Data Watermarking","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2601.18127","title":"The Limits of AI Data Transparency Policy: Three Disclosure Fallacies","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2605.06865","title":"Dataset Watermarking with Provable Black-Box Detection","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.24818","title":"Correcting test set contamination by spiking the training data","created":"2026-05-24","cited_by_count":0,"area":null}],"next_cursor":null}