mlsanityData to 7 Oct 2026

Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections

arXiv 2003.08529 ↗First posted 19 Mar 2020cs.CLcited by 21

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Abstract (arXiv)

Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they comprise. In this work, we propose metrics of diversity, density, and homogeneity that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. We conduct a series of simulations to verify that each metric holds desired properties and resonates with human intuitions. Experiments on real-world datasets demonstrate that the proposed characteristic metrics are highly correlated with text classification performance of a renowned model, BERT, which could inspire future applications.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2020.

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Citing papers per year: 2022: 2; 2023: 1; 2024: 6; 2025: 8; 2026: 4.

21 papers.

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  1. 2025Evaluating the Diversity and Quality of LLM Generated Contentcited by 51
  2. 2024A Comprehensive Survey on Data Augmentationcited by 38
  3. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  4. 2025Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metriccited by 24
  5. 2024IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognitioncited by 20
  6. 2022Measuring Datacited by 18
  7. 2023Metric Space Magnitude for Evaluating the Diversity of Latent Representationscited by 16
  8. 2025Diversity-oriented Data Augmentation with Large Language Modelscited by 7
  9. 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
  10. 2025DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoningcited by 6
  11. 2025Measuring Diversity in Synthetic Datasetscited by 5
  12. 2024CLUE: Concept-Level Uncertainty Estimation for Large Language Modelscited by 4
9 more
  1. 2025A survey of diversity quantification in natural language processing: The why, what, where and howcited by 4
  2. 2024Data Measurements for Decentralized Data Marketscited by 2
  3. 2025We Need to Measure Data Diversity in NLP -- Better and Broadercited by 2
  4. 2026emb-diversity: A Tool for Embedding-Based Measurement of Data Diversitycited by 1
  5. 2022Learning to Diversify for Product Question Generationcited by 0
  6. 2025Few-shot multi-token DreamBooth with LoRa for style-consistent character generationcited by 0
  7. 2026Measuring Dataset Diversity from a Geometric Perspectivecited by 0
  8. 2026Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generationcited by 0
  9. 2026LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesiscited by 0