Researchers from the University of Maryland Introduce GenQA Instruction Dataset: Automating Large-Scale Instruction Dataset Generation for AI Model Finetuning and Diversity Enhancement

Academic datasets are typically smaller, often comprising hundreds or thousands of samples, as they are limited by resources and the labor-intensive process of manual curation. On the other hand, industrial datasets can contain tens of millions of samples, benefiting from economies of scale and the ability to generate larger, more diverse datasets.

The challenges of relying on human-annotated data include the labor-intensive and costly process of manually curating datasets, which limits the scale and diversity of the data that can be generated. Additionally, human-annotated datasets often face issues such as grammatical errors and text quality problems, requiring human oversight. These challenges have driven researchers to explore automated methods for generating instruction datasets that rival the quality of those produced through human labor.