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data diversity

Data diversity refers to the degree of variety, heterogeneity, and distinctness among instances, features, or patterns contained within a dataset. In machine learning and dataset creation, it reflects how broadly a dataset spans different attributes, such as varied vocabulary, semantic structures, perspectives, or feature distributions, rather than repeating redundant or uniform samples. Maintaining high data diversity improves a model ability to generalize across unfamiliar scenarios and reduces bias, while typically requiring quality controls to ensure that the wide range of generated or collected examples remains accurate and relevant to the intended domain.

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Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions

John Joon Young Chung, Ece Kamar, Saleema Amershi

OrganizationsMicrosoftUniversity of Michigan

Why you should read this

Demonstrates how combining large language model diversification techniques with targeted human label replacement allows smaller downstream classifiers to outperform few-shot large language models by balancing synthetic text variety and annotation accuracy.

Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In this work, we explore human-AI partnerships to facilitate high diversity and accuracy in LLM-based text data generation. We first examine two approaches to diversify text generation: 1) logit suppression, which minimizes the generation of languages that have already been frequently generated, and 2) temperature sampling, which flattens the token sampling probability. We found that diversification approaches can increase data diversity but often at the cost of data accuracy (i.e., text and labels being appropriate for the target domain). To address this issue, we examined two human interventions, 1) label replacement (LR), correcting misaligned labels, and 2) out-of-scope filtering (OOSF), removing instances that are out of the user’s domain of interest or to which no considered label applies. With oracle studies, we found that LR increases the absolute accuracy of models trained with diversified datasets by 14.4%. Moreover, we found that some models trained with data generated with LR interventions outperformed LLM-based few-shot classification. In contrast, OOSF was not effective in increasing model accuracy, implying the need for future work in human-in-the-loop text data generation.

Added

2026-09-26