{"total":57,"rows":[{"id":"2404.18824","title":"Benchmarking Benchmark Leakage in Large Language Models","created":"2024-04-29","cited_by_count":106,"area":null},{"id":"2407.09007","title":"Benchmarking Language Model Creativity: A Case Study on Code Generation","created":"2024-07-12","cited_by_count":40,"area":null},{"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":"2403.04780","title":"Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining","created":"2024-03-02","cited_by_count":20,"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":"2409.14057","title":"Co-occurrence is not Factual Association in Language Models","created":"2024-09-21","cited_by_count":12,"area":null},{"id":"2502.07577","title":"Automated Capability Discovery via Foundation Model Self-Exploration","created":"2025-02-11","cited_by_count":10,"area":null},{"id":"2502.15823","title":"InductionBench: LLMs Fail in the Simplest Complexity Class","created":"2025-02-20","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":"2602.12966","title":"ProbeLLM: Automating Principled Diagnosis of LLM Failures","created":"2026-02-13","cited_by_count":9,"area":null},{"id":"2502.01683","title":"LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient","created":"2025-02-02","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":"2409.14302","title":"Reliable and diverse evaluation of LLM medical knowledge mastery","created":"2024-09-22","cited_by_count":6,"area":null},{"id":"2503.23361","title":"Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base","created":"2025-03-30","cited_by_count":6,"area":null},{"id":"2507.19219","title":"How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework","created":"2025-07-25","cited_by_count":6,"area":null},{"id":"2509.23061","title":"Local Success Does Not Compose: Benchmarking Large Language Models for Compositional Formal Verification","created":"2025-09-27","cited_by_count":6,"area":null},{"id":"2606.06462","title":"Benchmark Everything Everywhere All at Once","created":"2026-06-04","cited_by_count":6,"area":null},{"id":"2503.05049","title":"Dynamic-KGQA: A Scalable Framework for Generating Adaptive Question Answering Datasets","created":"2025-03-06","cited_by_count":5,"area":null},{"id":"2604.10988","title":"WebForge: Breaking the Realism-Reproducibility-Scalability Trilemma in Browser Agent Benchmark","created":"2026-04-13","cited_by_count":5,"area":null},{"id":"2410.11507","title":"TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains","created":"2024-10-15","cited_by_count":4,"area":null},{"id":"2502.12459","title":"Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2503.01986","title":"Adaptively profiling models with task elicitation","created":"2025-03-03","cited_by_count":4,"area":null},{"id":"2503.04840","title":"Framing the Game: How Context Shapes LLM Decision-Making","created":"2025-03-05","cited_by_count":4,"area":null},{"id":"2510.17725","title":"AcademicEval: Live Long-Context LLM Benchmark","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2503.10542","title":"Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2506.00963","title":"From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation","created":"2025-06-01","cited_by_count":3,"area":null},{"id":"2507.09701","title":"MCEval: A Dynamic Framework for Fair Multilingual Cultural Evaluation of LLMs","created":"2025-07-13","cited_by_count":3,"area":null},{"id":"2508.18905","title":"Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks","created":"2025-08-26","cited_by_count":3,"area":null},{"id":"2605.17829","title":"Interactive Evaluation Requires a Design Science","created":"2026-05-18","cited_by_count":3,"area":null},{"id":"2606.22437","title":"MMGist: A Comprehensive Multimodal Benchmark for 2027","created":"2026-06-21","cited_by_count":3,"area":null},{"id":"2503.04655","title":"CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2504.08300","title":"Large Language Models Could Be Rote Learners","created":"2025-04-11","cited_by_count":2,"area":null},{"id":"2504.14039","title":"MEQA: A Meta-Evaluation Framework for Question & Answer LLM Benchmarks","created":"2025-04-18","cited_by_count":2,"area":null},{"id":"2506.00741","title":"Data Swarms: Optimizable Generation of Synthetic Evaluation Data","created":"2025-05-31","cited_by_count":2,"area":null},{"id":"2510.19005","title":"Dynamic Evaluation for Oversensitivity in LLMs","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2511.09139","title":"MACEval: A Multi-Agent Continual Evaluation Network for Large Models","created":"2025-11-12","cited_by_count":2,"area":null},{"id":"2511.10691","title":"Evaluating from Benign to Dynamic Adversarial: A Squid Game for Large Language Models","created":"2025-11-12","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":"2503.16614","title":"Classification of User Reports for Detection of Faulty Computer Components using NLP Models: A Case Study","created":"2025-03-20","cited_by_count":1,"area":null},{"id":"2604.17842","title":"QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2609.32248","title":"Anytime-Valid LLM Leaderboards via Benchmark-weighted and Block-Factorized e-Processes","created":"2026-09-26","cited_by_count":1,"area":null},{"id":"2505.16646","title":"SMART: Self-Generating and Self-Validating Multi-Dimensional Assessment for LLMs' Mathematical Problem Solving","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2512.08965","title":"Financial Instruction Following Evaluation (FIFE)","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2602.13217","title":"VeRA: Renewing Reasoning Benchmarks with Executable Specifications","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2605.18824","title":"Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models","created":"2026-05-12","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":"2606.15258","title":"Mask-Proof: An LLM-based Automated Data Curation Pipeline on Mathematical Proofs","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.18642","title":"Spaghetti Architect: A Contamination-Resistant, By-Construction-Labelled, Multi-Language Code Dataset Generator","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2608.04397","title":"NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.06933","title":"Ask-E: An Environment for Calibrated Question Generation","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2608.15979","title":"ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.25352","title":"SSP-Bench: A Hybrid Data Generation Framework for Safety, Security, and Privacy Evaluation","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.28335","title":"An Open Pipeline and Dashboard for Systemic-Risk Evidence under the EU AI Act's Code of Practice","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}