mlsanityData to 7 Oct 2026

A Two-Sided Discussion of Preregistration of NLP Research

arXiv 2302.10086 ↗First posted 20 Feb 2023cs.CLcited by 6

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Abstract (arXiv)

Van Miltenburg et al. (2021) suggest NLP research should adopt preregistration to prevent fishing expeditions and to promote publication of negative results. At face value, this is a very reasonable suggestion, seemingly solving many methodological problems with NLP research. We discuss pros and cons -- some old, some new: a) Preregistration is challenged by the practice of retrieving hypotheses after the results are known; b) preregistration may bias NLP toward confirmatory research; c) preregistration must allow for reclassification of research as exploratory; d) preregistration may increase publication bias; e) preregistration may increase flag-planting; f) preregistration may increase p-hacking; and finally, g) preregistration may make us less risk tolerant. We cast our discussion as a dialogue, presenting both sides of the debate.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 90 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

13 of 90 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019How multilingual is Multilingual BERT?cited by 642
  3. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  4. 2020With Little Power Comes Great Responsibilitycited by 138
  5. 2018Troubling Trends in Machine Learning Scholarshipcited by 107
  6. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  7. 2020Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTscited by 71
  8. 2021A Systematic Review of Reproducibility Research in Natural Language Processingcited by 34
  9. 2017Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasetscited by 32
  10. 2022Sources of Irreproducibility in Machine Learning: A Reviewcited by 28
  11. 2021Preregistering NLP Researchcited by 24
  12. 2019HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learningcited by 13
1 more
  1. 2020Risk Assessment for Machine Learning Modelscited by 3

Built on by

Cited by year (2026 to 7 Oct)

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

6 papers.