Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM
  Fine-Tuning Data from Unstructured Documents
Authors: name: Ziyang Miao, name: Qiyu Sun, name: Jingyuan Wang, name: Yuchen Gong, name: Yaowei Zheng, name: Shiqi Li, name: Richong Zhang

Large language models (LLMs) have shown impressive performance on
general-purpose tasks, yet adapting them to specific domains remains
challenging due to the scarcity of high-quality domain data. Existing data
synthesis tools often struggle to extract reliable fine-tuning data from
heterogeneous documents effectively. To address this limitation, we propose
Easy Dataset, a unified framework for synthesizing fine-tuning data from
unstructured documents via an intuitive graphical user interface (GUI).
Specifically, Easy Dataset allows users to easily configure text extraction
models and chunking strategies to transform raw documents into coherent text
chunks. It then leverages a persona-driven prompting approach to generate
diverse question-answer pairs using public-available LLMs. Throughout the
pipeline, a human-in-the-loop visual interface facilitates the review and
refinement of intermediate outputs to ensure data quality. Experiments on a
financial question-answering task show that fine-tuning LLMs on the synthesized
dataset significantly improves domain-specific performance while preserving
general knowledge. The source code and installable package are available at
https://github.com/ConardLi/easy-dataset and have garnered over 9,000 GitHub
stars.