arXiv:2507.20858v4 Announce Type: replace Abstract: The concept of diversity received increasing attention in natural language processing (NLP) in recent years. It became an advocated property of datasets and systems, with many measures used to quantify it, but also many cross-paper inconsistencies. To address this fragmentation, we take inspiration from other scientific fields where diversity has been more thoroughly conceptualized. We build upon (Sarkar, 2010) and (Stirling, 2007), two unified cross-domain frameworks inspired from ecology, economy and other domains. The first one advocates explicit justification of diversity endorsement. The latter distinguishes three dimensions of diversity: variety, balance, and disparity. Surveying over 300 recent diversity-related papers from ACL Anthology, we propose an NLP-specific framework with 4 perspectives: why diversity is important, what diversity is measured on, where it is measured, and how. We find that in NLP richness (the simplest variety) and distance averaging (the simplest disparity) are the most prevalent measure families, and that pure balance measures are nearly absent. Moreover, half of the measures used in NLP do not conform to Stirling's framework. Our analysis increases comparability of approaches to diversity in NLP, allowing us to formulate recommendations for the field. We recommend that the field systematically use a unified terminology, and that it engage in debates about the desirable properties of diversity measures as well as about the normative assumptions behind their use.