用自动微调大模型从科学文献中提取结构化数据，提升知识转化效率。
ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity
- 基于DARWIN模型在标记数据极少时实现高精度提取
- 依托AWS搭建自动化流程，支持用户自定义模型开发
- 适合科研人员快速构建领域知识库，推动自然信息学发展
自然语言处理（NLP）广泛用于从长文本中提取结构化信息，但因科学文本的领域特性、复杂预处理及多层级设备级信息粒度，仍面临挑战。为此，我们提出ByteScience——一个非营利性的基于云的自动微调大语言模型平台，旨在从海量科学文献中提取结构化科学数据并合成新知识。该平台基于开源的科学专用微调大模型DARWIN，部署于亚马逊云服务（AWS），提供自动化、易用的定制模型开发与数据提取工作流。仅需少量高质量标注文章，即可实现显著精度。该工具有效缩短了科学文献到结构化知识的转化路径，助力自然信息学进步。
原文摘要 · Abstract (English)
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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