Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

TL;DR

Easy Dataset provides an accessible, GUI-based framework for synthesizing high-quality, domain-specific fine-tuning data from unstructured documents, enhancing LLM adaptation with human-in-the-loop review.

Contribution

It introduces a unified, user-friendly system for transforming raw documents into training data, combining configurable extraction, persona-driven prompting, and human review.

Findings

Improves domain-specific LLM performance on financial QA tasks.

Enables efficient data synthesis with minimal technical expertise.

Achieves high user engagement and data quality through visual interfaces.

Abstract

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.