  Materials language processing (MLP) is one of the key facilitators of
materials science research, as it enables the extraction of structured
information from massive materials science literature. Prior works suggested
high-performance MLP models for text classification, named entity recognition
(NER), and extractive question answering (QA), which require complex model
architecture, exhaustive fine-tuning and a large number of human-labelled
datasets. In this study, we develop generative pretrained transformer
(GPT)-enabled pipelines where the complex architectures of prior MLP models are
replaced with strategic designs of prompt engineering. First, we develop a
GPT-enabled document classification method for screening relevant documents,
achieving comparable accuracy and reliability compared to prior models, with
only small dataset. Secondly, for NER task, we design an entity-centric
prompts, and learning few-shot of them improved the performance on most of
entities in three open datasets. Finally, we develop an GPT-enabled extractive
QA model, which provides improved performance and shows the possibility of
automatically correcting annotations. While our findings confirm the potential
of GPT-enabled MLP models as well as their value in terms of reliability and
practicability, our scientific methods and systematic approach are applicable
to any materials science domain to accelerate the information extraction of
scientific literature.
