We Need to Measure Data Diversity in NLP -- Better and Broader

2025-05-26

AI Summary

This study addresses fundamental challenges in quantifying dataset diversity for NLP—including conceptual ambiguity, granularity mismatch, and poor cross-domain generalizability—by proposing the first interdisciplinary diversity evaluation framework integrating linguistics, sociology, and information theory. Through conceptual analysis and methodological critique, it rigorously defines three core dimensions: semantic distance, distributional shift, and demographic representation balance, thereby identifying three principal measurement challenges: definitional vagueness, scale misalignment, and the value-neutrality dilemma. The framework enables fine-grained modeling, empirically verifiable assessment, and task-adaptive calibration. It establishes a theoretical foundation for developing fairness-aware, reproducible, and interpretable diversity metrics, advancing dataset quality evaluation from heuristic judgment toward principled, scientific quantification.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures.

Problem

• Measuring data diversity in NLP effectively
• Addressing underexplored methodological challenges
• Incorporating interdisciplinary perspectives for better measures

Innovation

• Measure data diversity in NLP
• Address conceptual methodological challenges
• Apply interdisciplinary perspectives