{"total":60,"rows":[{"id":"2104.14337","title":"Dynabench: Rethinking Benchmarking in NLP","created":"2021-04-07","cited_by_count":459,"area":null},{"id":"2305.03514","title":"Can Large Language Models Transform Computational Social Science?","created":"2023-04-12","cited_by_count":250,"area":null},{"id":"2207.13332","title":"RealTime QA: What's the Answer Right Now?","created":"2022-07-27","cited_by_count":244,"area":null},{"id":"2102.01672","title":"The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics","created":"2021-02-02","cited_by_count":239,"area":null},{"id":"2102.01951","title":"Mind the Gap: Assessing Temporal Generalization in Neural Language Models","created":"2021-02-03","cited_by_count":230,"area":null},{"id":"2109.06157","title":"SituatedQA: Incorporating Extra-Linguistic Contexts into QA","created":"2021-09-13","cited_by_count":145,"area":null},{"id":"2310.15819","title":"Generative Language Models Exhibit Social Identity Biases","created":"2023-10-24","cited_by_count":111,"area":null},{"id":"2306.04618","title":"Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations","created":"2023-06-07","cited_by_count":97,"area":null},{"id":"2504.08165","title":"Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora","created":"2025-04-10","cited_by_count":88,"area":null},{"id":"2104.08678","title":"Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation","created":"2021-04-18","cited_by_count":79,"area":null},{"id":"2201.08277","title":"NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis","created":"2022-01-20","cited_by_count":64,"area":null},{"id":"2205.02894","title":"Interactive Model Cards: A Human-Centered Approach to Model Documentation","created":"2022-05-05","cited_by_count":61,"area":null},{"id":"2106.02280","title":"Human-Adversarial Visual Question Answering","created":"2021-06-04","cited_by_count":57,"area":null},{"id":"2211.00151","title":"A Close Look into the Calibration of Pre-trained Language Models","created":"2022-10-31","cited_by_count":57,"area":null},{"id":"2106.06052","title":"Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking","created":"2021-05-21","cited_by_count":53,"area":null},{"id":"2301.05036","title":"Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information","created":"2023-01-12","cited_by_count":52,"area":null},{"id":"2108.05921","title":"Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate","created":"2021-08-12","cited_by_count":50,"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":"2106.00872","title":"On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study","created":"2021-06-02","cited_by_count":31,"area":null},{"id":"2112.09062","title":"Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants","created":"2021-12-16","cited_by_count":31,"area":null},{"id":"2110.08514","title":"Analyzing Dynamic Adversarial Training Data in the Limit","created":"2021-10-16","cited_by_count":24,"area":null},{"id":"2305.08414","title":"What's the Meaning of Superhuman Performance in Today's NLU?","created":"2023-05-15","cited_by_count":23,"area":null},{"id":"2405.16747","title":"Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective","created":"2024-05-27","cited_by_count":22,"area":null},{"id":"2410.20245","title":"Improving Model Evaluation using SMART Filtering of Benchmark Datasets","created":"2024-10-26","cited_by_count":21,"area":null},{"id":"2311.01007","title":"Effective Human-AI Teams via Learned Natural Language Rules and Onboarding","created":"2023-11-02","cited_by_count":20,"area":null},{"id":"2505.21091","title":"Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)","created":"2025-05-27","cited_by_count":19,"area":null},{"id":"2304.13148","title":"Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection","created":"2023-04-25","cited_by_count":18,"area":null},{"id":"2204.01906","title":"Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks","created":"2022-04-05","cited_by_count":16,"area":null},{"id":"2210.03165","title":"A Theory of Dynamic Benchmarks","created":"2022-10-06","cited_by_count":16,"area":null},{"id":"2306.00639","title":"Being Right for Whose Right Reasons?","created":"2023-06-01","cited_by_count":12,"area":null},{"id":"2310.11906","title":"Rather a Nurse than a Physician -- Contrastive Explanations under Investigation","created":"2023-10-18","cited_by_count":11,"area":null},{"id":"2406.12928","title":"Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox","created":"2024-06-15","cited_by_count":11,"area":null},{"id":"2411.02988","title":"Confidence Calibration of Classifiers with Many Classes","created":"2024-11-05","cited_by_count":11,"area":null},{"id":"2110.08355","title":"Clean or Annotate: How to Spend a Limited Data Collection Budget","created":"2021-10-15","cited_by_count":10,"area":null},{"id":"2402.19472","title":"Efficient Lifelong Model Evaluation in an Era of Rapid Progress","created":"2024-02-29","cited_by_count":10,"area":null},{"id":"2210.06799","title":"Benchmarking Long-tail Generalization with Likelihood Splits","created":"2022-10-13","cited_by_count":9,"area":null},{"id":"2206.14729","title":"longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks","created":"2022-06-29","cited_by_count":8,"area":null},{"id":"2402.08225","title":"Improving Black-box Robustness with In-Context Rewriting","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2112.00086","title":"Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking","created":"2021-11-30","cited_by_count":7,"area":null},{"id":"2305.14384","title":"Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models","created":"2023-05-22","cited_by_count":7,"area":null},{"id":"2506.05979","title":"Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization","created":"2025-06-06","cited_by_count":4,"area":null},{"id":"2512.23701","title":"Eliciting Behaviors in Multi-Turn Conversations","created":"2025-12-29","cited_by_count":4,"area":null},{"id":"2109.04385","title":"Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification","created":"2021-09-09","cited_by_count":3,"area":null},{"id":"2209.02812","title":"Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation","created":"2022-09-06","cited_by_count":3,"area":null},{"id":"2312.04032","title":"RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training","created":"2023-12-07","cited_by_count":3,"area":null},{"id":"2406.14666","title":"Co-training for Low Resource Scientific Natural Language Inference","created":"2024-06-20","cited_by_count":3,"area":null},{"id":"2509.08960","title":"BRoverbs -- Measuring how much LLMs understand Portuguese proverbs","created":"2025-09-10","cited_by_count":3,"area":null},{"id":"2210.12233","title":"TCAB: A Large-Scale Text Classification Attack Benchmark","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2306.04925","title":"Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning","created":"2023-06-08","cited_by_count":2,"area":null},{"id":"2410.23472","title":"Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems","created":"2024-10-30","cited_by_count":2,"area":null},{"id":"2609.38827","title":"More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models","created":"2026-09-30","cited_by_count":2,"area":null},{"id":"2111.10497","title":"Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution","created":"2021-11-20","cited_by_count":1,"area":null},{"id":"2112.01054","title":"Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning","created":"2021-12-02","cited_by_count":1,"area":null},{"id":"2308.00528","title":"Unimodal Intermediate Training for Multimodal Meme Sentiment Classification","created":"2023-08-01","cited_by_count":1,"area":null},{"id":"2401.14027","title":"The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness","created":"2024-01-25","cited_by_count":1,"area":null},{"id":"2404.01029","title":"Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification","created":"2024-04-01","cited_by_count":1,"area":null},{"id":"2503.01235","title":"Your Model is Overconfident, and Other Lies We Tell Ourselves","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2407.12512","title":"$\\textit{GeoHard}$: Towards Measuring Class-wise Hardness through Modelling Class Semantics","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2501.00062","title":"ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis","created":"2024-12-29","cited_by_count":0,"area":null},{"id":"2608.06262","title":"Hypothesis Testing with Conditional Queries: Learnability and the Value of Interaction","created":"2026-08-06","cited_by_count":0,"area":null}],"next_cursor":null}