Evolving Stances on Reproducibility: A Longitudinal Study of NLP and ML Researchers’ Views and Experience of Reproducibility

Craig Thomson, Ehud Reiter, João Sedoc, Anya Belz


Abstract
Over the past 10 years in NLP/ML, as in other fields of science, there has been growing interest in, and work on, reproducibility and methods for improving it. Identical experiments producing different results can be due to variation between samples of evaluation items or evaluators, but it can also be due to poor experimental practice. Both can be mitigated by bringing multiple comparable studies together in systematic reviews that can draw conclusions beyond the level of the individual studies, but such systematic reviews barely exist in NLP/ML. The alternative is to focus on improving experimental practice and study-level reproducibility, and the first step in this direction is awareness of the importance of reproducibility and knowledge of how to improve it. Here we aim to assess (i) what NLP/ML practitioners’ current views and experience of reproducibility are, and (ii) to what extent they have changed over the past two years, a period of rapidly growing interest in reproducibility. We report for the first time, results from two identical surveys, the first carried out in 2022 and the second in 2024, each time surveying 149 NLP and ML researchers. The results from the 2024 survey assess i above. We then compare the results of the two surveys in order to address ii above. We find that views and experience overall are moving towards better practice and appreciation of reproducibility.
Anthology ID:
2025.findings-emnlp.1404
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25738–25760
Language:
URL:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1404/
DOI:
10.18653/v1/2025.findings-emnlp.1404
Bibkey:
Cite (ACL):
Craig Thomson, Ehud Reiter, João Sedoc, and Anya Belz. 2025. Evolving Stances on Reproducibility: A Longitudinal Study of NLP and ML Researchers’ Views and Experience of Reproducibility. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 25738–25760, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Evolving Stances on Reproducibility: A Longitudinal Study of NLP and ML Researchers’ Views and Experience of Reproducibility (Thomson et al., Findings 2025)
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PDF:
https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1404.pdf
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 2025.findings-emnlp.1404.checklist.pdf