DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM
  Workflows
Ajay Patel Colin Raffel Chris Callison-Burch
Large language models (LLMs) have become a dominant and important tool for
NLP researchers in a wide range of tasks. Today, many researchers use LLMs in
synthetic data generation, task evaluation, fine-tuning, distillation, and
other model-in-the-loop research workflows. However, challenges arise when
using these models that stem from their scale, their closed source nature, and
the lack of standardized tooling for these new and emerging workflows. The
rapid rise to prominence of these models and these unique challenges has had
immediate adverse impacts on open science and on the reproducibility of work
that uses them. In this paper, we introduce DataDreamer, an open source Python
library that allows researchers to write simple code to implement powerful LLM
workflows. DataDreamer also helps researchers adhere to best practices that we
propose to encourage open science and reproducibility. The library and
documentation are available at https://github.com/datadreamer-dev/DataDreamer .