mlsanityData to 7 Oct 2026

Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

arXiv 2204.01906 ↗First posted 5 Apr 2022cs.CL cs.AIcited by 16

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Abstract (arXiv)

We introduce Dynatask: an open source system for setting up custom NLP tasks that aims to greatly lower the technical knowledge and effort required for hosting and evaluating state-of-the-art NLP models, as well as for conducting model in the loop data collection with crowdworkers. Dynatask is integrated with Dynabench, a research platform for rethinking benchmarking in AI that facilitates human and model in the loop data collection and evaluation. To create a task, users only need to write a short task configuration file from which the relevant web interfaces and model hosting infrastructure are automatically generated. The system is available at https://dynabench.org/ and the full library can be found at https://github.com/facebookresearch/dynabench.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 5 Apr 2022.

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24 of 34 references.

References · 24

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  7. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  8. 2021Datasets: A Community Library for Natural Language Processingcited by 526
  9. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  10. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
  11. 2020Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detectioncited by 217
  12. 2020Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehensioncited by 131
12 more
  1. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 77
  2. 2021Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Modelscited by 74
  3. 2020DynaSent: A Dynamic Benchmark for Sentiment Analysiscited by 63
  4. 2021Human-Adversarial Visual Question Answeringcited by 57
  5. 2021Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarkingcited by 53
  6. 2021Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hatecited by 50
  7. 2020Targeting the Benchmark: On Methodology in Current Natural Language Processing Researchcited by 44
  8. 2019Quizbowl: The Case for Incremental Question Answeringcited by 36
  9. 2020ANLIzing the Adversarial Natural Language Inference Datasetcited by 36
  10. 2021Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistantscited by 31
  11. 2021On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Studycited by 30
  12. 2021Analyzing Dynamic Adversarial Training Data in the Limitcited by 24

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Citing papers per year: 2022: 4; 2023: 5; 2024: 3; 2025: 1; 2026: 3.

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