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arXiv · 2411.12000

ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity

Tong Xie·Hanzhi Zhang·Shaozhou Wang·Yuwei Wan·Imran Razzak·Chunyu Kit·Wenjie Zhang·Bram Hoex
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Abstract

Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientific text by NLP models remains a challenge because of its domain-specific nature to complex data preprocessing and the granularity of multi-layered device-level information. To address this, we introduce ByteScience, a non-profit cloud-based auto fine-tuned Large Language Model (LLM) platform, which is designed to extract structured scientific data and synthesize new scientific knowledge from vast scientific corpora. The platform capitalizes on DARWIN, an open-source, fine-tuned LLM dedicated to natural science. The platform was built on Amazon Web Services (AWS) and provides an automated, user-friendly workflow for custom model development and data extraction. The platform achieves remarkable accuracy with only a small amount of well-annotated articles. This innovative tool streamlines the transition from the science literature to structured knowledge and data and benefits the advancements in natural informatics.

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Tong Xie, Hanzhi Zhang, Shaozhou Wang, Yuwei Wan, Imran Razzak, Chunyu Kit, Wenjie Zhang, Bram Hoex. 2024-11-18. ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity. https://arxiv.org/abs/2411.12000

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