Semi-automatic annotation of materials science text accelerates data extraction from literature

The emergence of large language models has the potential to greatly accelerate data extraction from materials science literature, but annotating the text before extraction is often still a manual task. Researchers led by Taylor Sparks at the University of Utah developed a new method that harnesses the power of Google’s Gemini Pro to reduce the need for manual annotation.

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