A Two-Sided Discussion of Preregistration of NLP Research
Not on the map. Why?
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
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019How multilingual is Multilingual BERT?cited by 642
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
- 2020With Little Power Comes Great Responsibilitycited by 138
- 2018Troubling Trends in Machine Learning Scholarshipcited by 107
- 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
- 2020Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTscited by 71
- 2021A Systematic Review of Reproducibility Research in Natural Language Processingcited by 34
- 2017Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasetscited by 32
- 2022Sources of Irreproducibility in Machine Learning: A Reviewcited by 28
- 2021Preregistering NLP Researchcited by 24
- 2019HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learningcited by 13
1 more
- 2020Risk Assessment for Machine Learning Modelscited by 3
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 3.
6 papers.
- 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigorcited by 7
- 2023Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processingcited by 3
- 2026Scaffold, Not Vocabulary? A Controlled, Two-Tier, Pre-Registered Study of a Popperian Code-Generation Skillcited by 2
- 2024Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Studycited by 1
- 2026No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpuscited by 0
- 2026Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpointscited by 0
- 2026Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpoints3 Sep 2026
- 2026No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpus28 Aug 2026
- 2026Scaffold, Not Vocabulary? A Controlled, Two-Tier, Pre-Registered Study of a Popperian Code-Generation Skill4 Jun 2026
- 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor17 Jun 2025
- 2024Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study5 Apr 2024
- 2023Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing17 Apr 2023