Tracing Research Inequality in NLP: How Resource Disparities Shape Topic Trends and Methodological Diffusion via Citations

ACL ARR 2025 May Submission4821 Authors

Abstract:

The growing resource gap between institutions raises critical questions about transparency, replicability, and inclusiveness in AI research.
While some AI research topics remain accessible, research in areas such as large language models (LLMs) necessitate more resources such as computational power and data access: resources largely concentrated among industry companies and a few top universities. This study investigates research inequality in NLP by analyzing topic shifts, institutional resource gap, and citation intent patterns in papers from the ACL Anthology between 2010 and 2022. We identify 2016 as a critical turning point in NLP, marked by the rise of large language models (LLMs) and generative tasks, which have driven increased attention to topics such as Language Modeling, Generation, and Multimodality, while traditional areas like Machine Translation and Syntax/Parsing have declined. High-resource institutions are more likely to publish on these trending topics, as indicated by higher topic shift ratios. In contrast, low-resource teams are concentrated in declining topics. Citation intent analysis reveals that methodology-use citations, which indicate resource transfer, are decreasing over time, particularly in trending topics. This trend is especially pronounced in citations from low-resource to high-resource teams, suggesting that widening computational and infrastructural gaps limit the ability of low-resource institutions to adopt and build upon frontier research. These findings highlight a growing divide in NLP research participation and impact, underscoring the need for more inclusive and equitable research practices.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: NLP tools for social analysis, science of science, citation context analysis, research inequality

Contribution Types: Data analysis

Languages Studied: English

Submission Number: 4821