{"total":34,"rows":[{"id":"2406.19314","title":"LiveBench: A Challenging, Contamination-Limited LLM Benchmark","created":"2024-06-27","cited_by_count":262,"area":null},{"id":"2404.18824","title":"Benchmarking Benchmark Leakage in Large Language Models","created":"2024-04-29","cited_by_count":106,"area":null},{"id":"2402.09910","title":"DE-COP: Detecting Copyrighted Content in Language Models Training Data","created":"2024-02-15","cited_by_count":74,"area":null},{"id":"2405.16281","title":"ConStat: Performance-Based Contamination Detection in Large Language Models","created":"2024-05-25","cited_by_count":33,"area":null},{"id":"2402.02823","title":"Evading Data Contamination Detection for Language Models is (too) Easy","created":"2024-02-05","cited_by_count":30,"area":null},{"id":"2403.00393","title":"TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs","created":"2024-03-01","cited_by_count":24,"area":null},{"id":"2409.09554","title":"ASR Error Correction using Large Language Models","created":"2024-09-14","cited_by_count":24,"area":null},{"id":"2405.20962","title":"Large Language Models are Zero-Shot Next Location Predictors","created":"2024-05-31","cited_by_count":22,"area":null},{"id":"2503.17514","title":"Language Models May Verbatim Complete Text They Were Not Explicitly Trained On","created":"2025-03-21","cited_by_count":21,"area":null},{"id":"2409.10566","title":"Eureka: Evaluating and Understanding Large Foundation Models","created":"2024-09-13","cited_by_count":18,"area":null},{"id":"2503.16402","title":"The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination","created":"2025-03-20","cited_by_count":15,"area":null},{"id":"2404.00699","title":"A Comprehensive Survey of Contamination Detection Methods in Large Language Models","created":"2024-03-31","cited_by_count":12,"area":null},{"id":"2412.18719","title":"Using Large Language Models for Automated Grading of Student Writing about Science","created":"2024-12-25","cited_by_count":12,"area":null},{"id":"2504.21303","title":"Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges","created":"2025-04-30","cited_by_count":8,"area":null},{"id":"2505.18102","title":"CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting","created":"2025-05-23","cited_by_count":7,"area":null},{"id":"2408.11546","title":"Memorization in In-Context Learning","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2502.17259","title":"Detecting Benchmark Contamination Through Watermarking","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2509.01082","title":"RefineStat: Efficient Exploration for Probabilistic Program Synthesis","created":"2025-09-01","cited_by_count":5,"area":null},{"id":"2510.20351","title":"When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets","created":"2025-10-23","cited_by_count":3,"area":null},{"id":"2602.16729","title":"Intent Laundering: AI Safety Datasets Are Not What They Seem","created":"2026-02-17","cited_by_count":3,"area":null},{"id":"2402.03776","title":"Large Language Models As MOOCs Graders","created":"2024-02-06","cited_by_count":2,"area":null},{"id":"2509.20909","title":"LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories","created":"2025-09-25","cited_by_count":2,"area":null},{"id":"2601.04301","title":"Quantifying the Effect of Test Set Contamination on Generative Evaluations","created":"2026-01-07","cited_by_count":2,"area":null},{"id":"2412.19361","title":"Dynamic Skill Adaptation for Large Language Models","created":"2024-12-26","cited_by_count":1,"area":null},{"id":"2501.13983","title":"AdEval: Alignment-based Dynamic Evaluation to Mitigate Data Contamination in Large Language Models","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2510.16022","title":"Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2601.00575","title":"InfoSynth: Information-Guided Benchmark Synthesis for LLMs","created":"2026-01-02","cited_by_count":1,"area":null},{"id":"2607.22554","title":"Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2609.09219","title":"Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents","created":"2026-09-07","cited_by_count":1,"area":null},{"id":"2603.27355","title":"LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications","created":"2026-03-28","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},{"id":"2608.12771","title":"Memorization Diagnostics for Code LLMs Should be Scale-Aware","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.19003","title":"Structure, Association, and Decision Value: Representation-Based Difficulty Estimation for Adaptive Inference in African-Language NLI","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.21888","title":"Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective","created":"2026-09-18","cited_by_count":0,"area":null}],"next_cursor":null}