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

TL;DR

This paper investigates the extent to which NLP research relies on industry-produced models and artifacts, revealing a significant industry influence that raises questions about research independence and potential corporate capture.

Contribution

It provides the first empirical analysis quantifying industry reliance in NLP research through a survey of 100 EMNLP 2022 papers, highlighting the dominance of industry artifacts.

Findings

Industry citations are at least three times higher than expected.

Most NLP research heavily depends on industry models and contributions.

The study offers a framework to assess collaboration versus corporate capture.

Abstract

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.