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Extracting Material Property Measurements from Scientific Literature with Limited Annotations.

Jessica Kong1, Gihan Panapitiya1, Emily Saldanha1

  • 1Pacific Northwest National Laboratory, Richland, Washington 99354 , United States.

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Extracting material property data is crucial for science. Large language models (LLMs) like GPT-4o show promise in reducing data labeling for named entity recognition (NER) and improving accuracy.

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Area of Science:

  • Chemistry
  • Materials Science
  • Data Science

Background:

  • Extracting material property data from scientific literature is essential for data-driven research.
  • Traditional methods using supervised named entity recognition (NER) require extensive data annotation, posing a significant barrier.
  • Developing efficient methods for specialized data extraction is a key challenge.

Purpose of the Study:

  • To compare conventional supervised NER with few-shot learning and large language model (LLM) approaches for material property extraction.
  • To evaluate the effectiveness of LLMs in reducing the need for large labeled training datasets.
  • To assess the performance of GPT-4o in direct property extraction and data augmentation.

Main Methods:

  • Comparative analysis of supervised NER, few-shot learning architectures, and LLM-based methods.
  • Utilizing GPT-4o for direct material property extraction with limited examples.
  • Employing LLMs for data augmentation to enhance supervised learning models.
  • Conducting error and data quality assessments.

Main Results:

  • The best-performing LLM (GPT-4o) effectively extracts material properties using limited examples.
  • LLMs, particularly GPT-4o, can significantly enhance supervised learning through data augmentation.
  • LLM-based approaches mitigate the barrier of extensive data labeling for NER tasks.
  • Error and data quality analyses provide insights into extraction performance factors.

Conclusions:

  • LLMs offer a powerful alternative to traditional supervised NER for material property extraction.
  • GPT-4o demonstrates superior performance in both direct extraction and augmenting supervised models.
  • These findings pave the way for more efficient and scalable data extraction in materials science and chemistry.