  We propose an instruction-based process for trustworthy data curation in
materials science (MatSci-Instruct), which we then apply to finetune a
LLaMa-based language model targeted for materials science (HoneyBee).
MatSci-Instruct helps alleviate the scarcity of relevant, high-quality
materials science textual data available in the open literature, and HoneyBee
is the first billion-parameter language model specialized to materials science.
In MatSci-Instruct we improve the trustworthiness of generated data by
prompting multiple commercially available large language models for generation
with an Instructor module (e.g. Chat-GPT) and verification from an independent
Verifier module (e.g. Claude). Using MatSci-Instruct, we construct a dataset of
multiple tasks and measure the quality of our dataset along multiple
dimensions, including accuracy against known facts, relevance to materials
science, as well as completeness and reasonableness of the data. Moreover, we
iteratively generate more targeted instructions and instruction-data in a
finetuning-evaluation-feedback loop leading to progressively better performance
for our finetuned HoneyBee models. Our evaluation on the MatSci-NLP benchmark
shows HoneyBee's outperformance of existing language models on materials
science tasks and iterative improvement in successive stages of
instruction-data refinement. We study the quality of HoneyBee's language
modeling through automatic evaluation and analyze case studies to further
understand the model's capabilities and limitations. Our code and relevant
datasets are publicly available at
\url{https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee}.
