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Annotating Materials Science Text: A Semi-automated Approach for Crafting Outputs with Gemini Pro

  • Thematic Section: Harnessing the Power of Materials Data
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Integrating Materials and Manufacturing Innovation Aims and scope Submit manuscript

Abstract

Recent advancements in large language models (LLMs) have paved the way for automated information extraction in the materials science domain. However, fine-tuning these models, crucial for effective machine learning pipelines in materials science, is hindered by a lack of pre-annotated data. Manual annotation, a laborious process, exacerbates the challenge. To address this, we introduce a tailored semi-automated annotation process, using Google’s Gemini Pro language model. Our approach focuses on two key tasks: extracting information in structured JSON format and generating abstractive summaries from materials science texts. The collaborative process, a symbiotic effort between human annotators and the LLM, driven by structured prompts and user-guided examples, enhances the annotation quality and augments the LLM’s capacity to comprehend materials science intricacies. Importantly, it streamlines human annotation efforts by leveraging the LLM’s proficient starting point.

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Acknowledgements

We acknowledge the assistance provided by ChatGPT, which was used for rephrasing and achieving coherence. However, it is important to note that all core ideas, text, tables, and figures were the original work of the authors. This research was supported by the National Science Foundation (NSF) under grant number DMR 2334411. We extend our appreciation to the NSF for their financial support, which made this study possible.

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  1. Department of Materials Science & Engineering, University of Utah, Salt Lake City, UT, 84112, USA

    Hasan M. Sayeed, Trupti Mohanty & Taylor D. Sparks

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  1. Hasan M. Sayeed
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  2. Trupti Mohanty
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  3. Taylor D. Sparks
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Correspondence to Taylor D. Sparks.

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Sayeed, H.M., Mohanty, T. & Sparks, T.D. Annotating Materials Science Text: A Semi-automated Approach for Crafting Outputs with Gemini Pro. Integr Mater Manuf Innov (2024). https://doi.org/10.1007/s40192-024-00356-4

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