mlsanityData to 14 Oct 2026

Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions

arXiv 2312.03912 ↗First posted 6 Dec 2023cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

Impressive performance of pre-trained models has garnered public attention and made news headlines in recent years. Almost always, these models are produced by or in collaboration with industry. Using them is critical for competing on natural language processing (NLP) benchmarks and correspondingly to stay relevant in NLP research. We surveyed 100 papers published at EMNLP 2022 to determine the degree to which researchers rely on industry models, other artifacts, and contributions to publish in prestigious NLP venues and found that the ratio of their citation is at least three times greater than what would be expected. Our work serves as a scaffold to enable future researchers to more accurately address whether: 1) Collaboration with industry is still collaboration in the absence of an alternative or 2) if NLP inquiry has been captured by the motivations and research direction of private corporations.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

14 of 41 references.

References · 14

  1. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  2. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  3. 2021What Will it Take to Fix Benchmarking in Natural Language Understanding?cited by 203
  4. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 169
  5. 2020With Little Power Comes Great Responsibilitycited by 142
  6. 2020The Cost of Training NLP Models: A Concise Overviewcited by 98
  7. 2021Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Paperscited by 71
  8. 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
  9. 2020The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integritycited by 38
  10. 2022deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networkscited by 28
  11. 2022What Do NLP Researchers Believe? Results of the NLP Community Metasurveycited by 28
  12. 2023The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Researchcited by 27
2 more
  1. 2023We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fieldscited by 20
  2. 2018Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approachescited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.