To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
Submission Type: Regular Long Paper
Submission Track: Theme Track: Large Language Models and the Future of NLP
Keywords: paradigm shift, future of nlp research, incentives, benchmarking, software, science of science
TL;DR: We study how recent paradigm shifts in NLP are similar and different from historical shifts in the field.
Abstract: NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception.
In this work, we seek to understand how to shape our future by better understanding our past.
We study factors that shape NLP as a field, including culture, incentives, and infrastructure by conducting long-form interviews with 26 NLP researchers of varying seniority, research area, institution, and social identity.
Our interviewees identify cyclical patterns in the field, as well as new shifts without historical parallel, including changes in benchmark culture and software infrastructure.
We complement this discussion with quantitative analysis of citation, authorship, and language use in the ACL Anthology over time.
We conclude by discussing shared visions, concerns, and hopes for the future of NLP.
We hope that this study of our field's past and present can prompt informed discussion of our community's implicit norms and more deliberate action to consciously shape the future.
Submission Number: 2155
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