{"total":259,"rows":[{"id":"2407.10671","title":"Qwen2 Technical Report","created":"2024-07-15","cited_by_count":2120,"area":null},{"id":"2406.11939","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","created":"2024-06-17","cited_by_count":500,"area":null},{"id":"2311.01964","title":"Don't Make Your LLM an Evaluation Benchmark Cheater","created":"2023-11-03","cited_by_count":194,"area":null},{"id":"2312.15166","title":"SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling","created":"2023-12-23","cited_by_count":172,"area":null},{"id":"2406.04244","title":"Benchmark Data Contamination of Large Language Models: A Survey","created":"2024-06-06","cited_by_count":147,"area":null},{"id":"2311.09783","title":"Investigating Data Contamination in Modern Benchmarks for Large Language Models","created":"2023-11-16","cited_by_count":138,"area":null},{"id":"2409.19839","title":"ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities","created":"2024-09-30","cited_by_count":110,"area":null},{"id":"2402.18060","title":"Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions","created":"2024-02-28","cited_by_count":109,"area":null},{"id":"2312.16337","title":"Task Contamination: Language Models May Not Be Few-Shot Anymore","created":"2023-12-26","cited_by_count":99,"area":null},{"id":"2407.04069","title":"A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations","created":"2024-07-04","cited_by_count":85,"area":null},{"id":"2406.16264","title":"One Thousand and One Pairs: A \"novel\" challenge for long-context language models","created":"2024-06-24","cited_by_count":80,"area":null},{"id":"2603.21687","title":"MIRAGE: The Illusion of Visual Understanding","created":"2026-03-23","cited_by_count":65,"area":null},{"id":"2312.12343","title":"LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction","created":"2023-12-19","cited_by_count":58,"area":null},{"id":"2404.13813","title":"From LLM to NMT: Advancing Low-Resource Machine Translation with Claude","created":"2024-04-22","cited_by_count":58,"area":null},{"id":"2404.07544","title":"From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples","created":"2024-04-11","cited_by_count":57,"area":null},{"id":"2405.01976","title":"Conformal Prediction for Natural Language Processing: A Survey","created":"2024-05-03","cited_by_count":53,"area":null},{"id":"2502.14127","title":"Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above","created":"2025-02-19","cited_by_count":52,"area":null},{"id":"2311.06233","title":"Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models","created":"2023-11-10","cited_by_count":47,"area":null},{"id":"2402.10104","title":"GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving","created":"2024-02-15","cited_by_count":45,"area":null},{"id":"2412.13670","title":"AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge","created":"2024-12-18","cited_by_count":44,"area":null},{"id":"2403.20266","title":"Latxa: An Open Language Model and Evaluation Suite for Basque","created":"2024-03-29","cited_by_count":42,"area":null},{"id":"2404.18443","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","created":"2024-04-29","cited_by_count":38,"area":null},{"id":"2402.10790","title":"In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss","created":"2024-02-16","cited_by_count":36,"area":null},{"id":"2402.15043","title":"KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models","created":"2024-02-23","cited_by_count":36,"area":null},{"id":"2509.03730","title":"The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs","created":"2025-09-03","cited_by_count":36,"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":"2407.07890","title":"Training on the Test Task Confounds Evaluation and Emergence","created":"2024-07-10","cited_by_count":34,"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":"2411.03923","title":"Evaluation data contamination in LLMs: how do we measure it and (when) does it matter?","created":"2024-11-06","cited_by_count":32,"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":"2403.02990","title":"Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges","created":"2024-03-05","cited_by_count":30,"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":"2408.08808","title":"Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge","created":"2024-08-16","cited_by_count":30,"area":null},{"id":"2406.17681","title":"VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation","created":"2024-06-25","cited_by_count":29,"area":null},{"id":"2405.11430","title":"MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation","created":"2024-05-19","cited_by_count":26,"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":"2406.01333","title":"Probing Language Models for Pre-training Data Detection","created":"2024-06-03","cited_by_count":25,"area":null},{"id":"2401.15042","title":"PROXYQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models","created":"2024-01-26","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":"2402.12025","title":"Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?","created":"2024-02-19","cited_by_count":23,"area":null},{"id":"2405.20574","title":"Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark","created":"2024-05-31","cited_by_count":23,"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":"2502.04314","title":"BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation","created":"2025-02-06","cited_by_count":22,"area":null},{"id":"2402.08100","title":"Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation","created":"2024-02-12","cited_by_count":21,"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":"2407.21530","title":"Data Contamination Report from the 2024 CONDA Shared Task","created":"2024-07-31","cited_by_count":20,"area":null},{"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":"2411.07127","title":"Benchmarking LLMs' Judgments with No Gold Standard","created":"2024-11-11","cited_by_count":19,"area":null},{"id":"2411.08324","title":"Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle","created":"2024-11-13","cited_by_count":19,"area":null},{"id":"2409.09927","title":"Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges","created":"2024-09-16","cited_by_count":18,"area":null},{"id":"2406.15586","title":"TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings","created":"2024-06-21","cited_by_count":17,"area":null},{"id":"2410.02110","title":"Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments","created":"2024-10-03","cited_by_count":17,"area":null},{"id":"2501.17858","title":"Improving Your Model Ranking on Chatbot Arena by Vote Rigging","created":"2025-01-29","cited_by_count":17,"area":null},{"id":"2605.10500","title":"SkillEvolver: Skill Learning as a Meta-Skill","created":"2026-05-11","cited_by_count":16,"area":null},{"id":"2405.15077","title":"Eliciting Informative Text Evaluations with Large Language Models","created":"2024-05-23","cited_by_count":15,"area":null},{"id":"2406.08787","title":"A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices","created":"2024-06-13","cited_by_count":15,"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":"2402.13125","title":"TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning","created":"2024-02-20","cited_by_count":14,"area":null},{"id":"2410.09247","title":"Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts","created":"2024-10-11","cited_by_count":14,"area":null},{"id":"2506.21614","title":"LastingBench: Defend Benchmarks Against Knowledge Leakage","created":"2025-06-21","cited_by_count":14,"area":null},{"id":"2511.21843","title":"FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers","created":"2025-11-26","cited_by_count":14,"area":null},{"id":"2404.17047","title":"Near to Mid-term Risks and Opportunities of Open-Source Generative AI","created":"2024-04-25","cited_by_count":13,"area":null},{"id":"2405.08597","title":"Risks and Opportunities of Open-Source Generative AI","created":"2024-05-14","cited_by_count":13,"area":null},{"id":"2405.19740","title":"PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations","created":"2024-05-30","cited_by_count":13,"area":null},{"id":"2601.03986","title":"Benchmark^2: Systematic Evaluation of LLM Benchmarks","created":"2026-01-07","cited_by_count":13,"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":"2410.13502","title":"MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs","created":"2024-10-17","cited_by_count":12,"area":null},{"id":"2402.13109","title":"CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models","created":"2024-02-20","cited_by_count":11,"area":null},{"id":"2403.01929","title":"Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?","created":"2024-03-04","cited_by_count":11,"area":null},{"id":"2407.08716","title":"A Taxonomy for Data Contamination in Large Language Models","created":"2024-07-11","cited_by_count":11,"area":null},{"id":"2502.18316","title":"WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging","created":"2025-02-25","cited_by_count":11,"area":null},{"id":"2502.11368","title":"LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing","created":"2025-02-17","cited_by_count":10,"area":null},{"id":"2507.11405","title":"DCR: Quantifying Data Contamination in LLMs Evaluation","created":"2025-07-15","cited_by_count":10,"area":null},{"id":"2403.00794","title":"Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models","created":"2024-02-23","cited_by_count":9,"area":null},{"id":"2409.00921","title":"Statically Contextualizing Large Language Models with Typed Holes","created":"2024-09-02","cited_by_count":9,"area":null},{"id":"2505.22251","title":"Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition","created":"2025-05-28","cited_by_count":9,"area":null},{"id":"2506.03231","title":"NetArena: Dynamic Benchmarks for AI Agents in Network Automation","created":"2025-06-03","cited_by_count":9,"area":null},{"id":"2512.04062","title":"Eval Factsheets: A Structured Framework for Documenting AI Evaluations","created":"2025-12-03","cited_by_count":9,"area":null},{"id":"2609.37647","title":"Evaluating and Benchmarking the System One Model Jev","created":"2026-09-29","cited_by_count":9,"area":null},{"id":"2403.09259","title":"To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation","created":"2024-03-14","cited_by_count":8,"area":null},{"id":"2405.11577","title":"A Multi-Perspective Analysis of Memorization in Large Language Models","created":"2024-05-19","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":"2504.13416","title":"STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings","created":"2025-04-18","cited_by_count":8,"area":null},{"id":"2505.00612","title":"Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation","created":"2025-05-01","cited_by_count":8,"area":null},{"id":"2509.11514","title":"LVLMs are Bad at Overhearing Human Referential Communication","created":"2025-09-15","cited_by_count":8,"area":null},{"id":"2404.06003","title":"FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models","created":"2024-04-09","cited_by_count":7,"area":null},{"id":"2406.00343","title":"Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios","created":"2024-06-01","cited_by_count":7,"area":null},{"id":"2406.16801","title":"RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale","created":"2024-06-24","cited_by_count":7,"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":"2507.17747","title":"Pretraining on the Test Set Is No Longer All You Need: A Debate-Driven Approach to QA Benchmarks","created":"2025-07-23","cited_by_count":7,"area":null},{"id":"2508.17416","title":"Data Leakage in Visual Datasets","created":"2025-08-24","cited_by_count":7,"area":null},{"id":"2510.12110","title":"Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models","created":"2025-10-14","cited_by_count":7,"area":null},{"id":"2406.16382","title":"UNO Arena for Evaluating Sequential Decision-Making Capability of Large Language Models","created":"2024-06-24","cited_by_count":6,"area":null},{"id":"2406.18326","title":"PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models","created":"2024-06-26","cited_by_count":6,"area":null},{"id":"2409.11149","title":"SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration","created":"2024-09-17","cited_by_count":6,"area":null},{"id":"2501.17830","title":"A Comprehensive Survey on Legal Summarization: Challenges and Future Directions","created":"2025-01-29","cited_by_count":6,"area":null},{"id":"2510.09259","title":"Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models","created":"2025-10-10","cited_by_count":6,"area":null},{"id":"2601.13717","title":"Simulated Ignorance Fails: A Systematic Study of LLM Behaviors on Forecasting Problems Before Model Knowledge Cutoff","created":"2026-01-20","cited_by_count":6,"area":null},{"id":"2402.00861","title":"Evaluating Large Language Models for Generalization and Robustness via Data Compression","created":"2024-02-01","cited_by_count":5,"area":null},{"id":"2406.10786","title":"Evaluating LLMs with Multiple Problems at once","created":"2024-06-16","cited_by_count":5,"area":null}],"next_cursor":"100"}