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Many recent improvements in NLP stem from the development and use of largepre-trained language models (PLMs) with billions of parameters. Large modelsizes makes computational cost one of the main limiting factors for trainingand evaluating such models; and has raised severe concerns about thesustainability, reproducibility, and inclusiveness for researching PLMs. Theseconcerns are often based on personal experiences and observations. However,there had not been any large-scale surveys that investigate them. In this work,we provide a first attempt to quantify these concerns regarding three topics,namely, environmental impact, equity, and impact on peer reviewing. Byconducting a survey with 312 participants from the NLP community, we captureexisting (dis)parities between different and within groups with respect toseniority, academia, and industry; and their impact on the peer reviewingprocess. For each topic, we provide an analysis and devise recommendations tomitigate found disparities, some of which already successfully implemented.Finally, we discuss additional concerns raised by many participants infree-text responses.

Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research