  This study examines the tendency to cite older work across 20 fields of study
over 43 years (1980--2023). We put NLP's propensity to cite older work in the
context of these 20 other fields to analyze whether NLP shows similar temporal
citation patterns to these other fields over time or whether differences can be
observed. Our analysis, based on a dataset of approximately 240 million papers,
reveals a broader scientific trend: many fields have markedly declined in
citing older works (e.g., psychology, computer science). We term this decline a
'citation age recession', analogous to how economists define periods of reduced
economic activity. The trend is strongest in NLP and ML research (-12.8% and
-5.5% in citation age from previous peaks). Our results suggest that citing
more recent works is not directly driven by the growth in publication rates
(-3.4% across fields; -5.2% in humanities; -5.5% in formal sciences) -- even
when controlling for an increase in the volume of papers. Our findings raise
questions about the scientific community's engagement with past literature,
particularly for NLP, and the potential consequences of neglecting older but
relevant research. The data and a demo showcasing our results are publicly
available.
