Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM
  Fine-Tuning Data from Unstructured Documents
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.
686cd234cc230c60b4100aec
Ziyang Miao
686cd234cc230c60b4100aed
Qiyu Sun
686cd234cc230c60b4100aee
Jingyuan Wang
686cd234cc230c60b4100aef
Yuchen Gong
claimed_verified
2025-07-08T09:05:42.785Z
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GYC
user
oGYCo
686cd234cc230c60b4100af0
Yaowei Zheng
claimed_verified
2025-07-08T08:39:35.178Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/642fef28a043f0ac7defa8a9/RwOEkuj3fOnOA54tGR7Ea.png
Yaowei Zheng
user
hiyouga
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Shiqi Li
claimed_verified
2025-07-17T08:21:10.076Z
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/avatars/33d7e667f50141fb426a9cfc69196547.svg
Li
user
Conard
686cd234cc230c60b4100af2
Richong Zhang
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM
  Fine-Tuning Data from Unstructured Documents
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.
A framework called Easy Dataset synthesizes fine-tuning data from unstructured documents using a GUI and LLMs, improving domain-specific performance of LLMs while maintaining general knowledge.
large language models fine-tuning domain-specific performance data synthesis graphical user interface text extraction models chunking strategies persona-driven prompting human-in-the-loop question-answer pairs