emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity

Abstract:There is growing evidence that data diversity is crucial for developing fair and robust NLP models. However, current approaches to measure diversity remain inconsistent and fragmented: While there exist a number of tools for measuring the lexical diversity of texts, researchers lack standardized tools for quantifying diversity based on embeddings. Embedding-based diversity measures are highly flexible: They work with any embedding model and any data that can be embedded, and are thus applicable to many notions of diversity. With emb-diversity, we provide a comprehensive embedding-based diversity measurement tool, spanning a broad range of measures. We demonstrate its potential for several use cases: measuring the stylistic, semantic, language and speaker diversity of datasets. this https URL

Computation and Language (cs.CL)

arXiv:2607.19848 [cs.CL]

(or arXiv:2607.19848v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.19848 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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