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Extracting accurate materials data from research papers with conversational language models and prompt engineering.

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ChatExtract automates data extraction from research papers using conversational large language models (LLMs). This method achieves high accuracy with minimal user effort, overcoming factual inaccuracies common in LLMs.

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Materials Science Data Management

Background:

  • Manual data extraction from research papers is time-consuming and labor-intensive.
  • Existing automated methods, including early language models, require significant expertise and coding.
  • Large Language Models (LLMs) offer potential for improved automated data extraction but face challenges with accuracy.

Purpose of the Study:

  • To introduce ChatExtract, a novel method for fully automated, accurate data extraction from research papers.
  • To minimize the initial effort and technical background required for data extraction.
  • To leverage conversational LLMs for efficient and reliable data retrieval.

Main Methods:

  • Engineered prompts applied to conversational LLMs to identify and extract data.
  • Utilized follow-up questions to verify data accuracy and mitigate LLM factual errors.
  • Tested ChatExtract on materials science data, including critical cooling rates and yield strengths.

Main Results:

  • ChatExtract achieved high data extraction accuracy, with precision and recall close to 90% using advanced LLMs like GPT-4.
  • The method demonstrated effectiveness in overcoming LLM factual inaccuracies through conversational verification.
  • Developed databases for metallic glasses' critical cooling rates and high entropy alloys' yield strengths.

Conclusions:

  • ChatExtract offers a simple, transferable, and accurate approach to automated data extraction.
  • Conversational LLMs, combined with strategic prompting, significantly enhance data extraction reliability.
  • This method is poised to become a powerful tool for data extraction in scientific research.