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OmniExtract: An automatic data extraction tool based on Large Language Model and Prompt Engineering

Wang, Y.; Tang, B.; Wu, S.; Meng, Y.; Kong, D.; Zhao, W.

2025-09-13 bioinformatics
10.1101/2025.09.11.675332 bioRxiv
Show abstract

Extracting structured information from documents or scientific papers is crucial for data sharing and retrieval. Recently, Large Language Model (LLM) has shown its impressive ability in text understanding and several tools based on LLM has been developed. However, its still difficult to find a universal and user-friendly tool for various practical extraction tasks. To address this challenge, we propose OmniExtract, an automatic data extraction tool with user-friendly configuration files which can adapt to various data extraction tasks. OmniExtract uses a prompt optimized engineering to improve prompt and obtain high performance, and it can support a comprehensive data extraction including text and tables. Evaluation results show that OmniExtract obtains a high accuracy over 80% for 3 datasets. Furthermore, two additional data extraction applications using OmniExtract have been provided, achieving an accuracy of 92.21% and an average F1 score of 0.83 respectively. The data reliability performance shows that OmniExtract is a valuable tool for database updating.

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The top 7 journals account for 50% of the predicted probability mass.

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50% of probability mass above
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.