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

TL;DR

This paper investigates methods to enhance diversity in text data generated by large language models while maintaining accuracy through human interventions, aiming to improve training datasets for better model performance.

Contribution

It introduces diversification techniques and human intervention strategies, demonstrating how label correction can significantly boost data accuracy in LLM-generated datasets.

Findings

Diversification increases data variety but can reduce accuracy.

Label replacement improves model accuracy by 14.4%.

Human interventions can outperform few-shot learning with LLMs.

Abstract

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.