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        <title>Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields</title>
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            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
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    <abstract>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 them over time or whether differences can be observed. Our analysis, based on a dataset of ~240 million papers, reveals a broader scientific trend: many fields have markedly declined in citing older works (e.g., psychology, computer science). 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.</abstract>
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%0 Conference Proceedings
%T Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields
%A Wahle, Jan Philip
%A Lima Ruas, Terry
%A Abdalla, Mohamed
%A Gipp, Bela
%A Mohammad, Saif M.
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wahle-etal-2025-citation
%X 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 them over time or whether differences can be observed. Our analysis, based on a dataset of ~240 million papers, reveals a broader scientific trend: many fields have markedly declined in citing older works (e.g., psychology, computer science). 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.
%U https://aclanthology.org/2025.coling-main.69/
%P 1027-1044

Markdown (Informal)

[Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://aclanthology.org/2025.coling-main.69/) (Wahle et al., COLING 2025)

• Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields (Wahle et al., COLING 2025)

ACL

• Jan Philip Wahle, Terry Lima Ruas, Mohamed Abdalla, Bela Gipp, and Saif M. Mohammad. 2025. Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields. In Proceedings of the 31st International Conference on Computational Linguistics, pages 1027–1044, Abu Dhabi, UAE. Association for Computational Linguistics.