PLLaMa: An Open-source Large Language Model for Plant Science
Xianjun Yang Junfeng Gao Wenxin Xue Erik Alexandersson
Large Language Models (LLMs) have exhibited remarkable capabilities in
understanding and interacting with natural language across various sectors.
However, their effectiveness is limited in specialized areas requiring high
accuracy, such as plant science, due to a lack of specific expertise in these
fields. This paper introduces PLLaMa, an open-source language model that
evolved from LLaMa-2. It's enhanced with a comprehensive database, comprising
more than 1.5 million scholarly articles in plant science. This development
significantly enriches PLLaMa with extensive knowledge and proficiency in plant
and agricultural sciences. Our initial tests, involving specific datasets
related to plants and agriculture, show that PLLaMa substantially improves its
understanding of plant science-related topics. Moreover, we have formed an
international panel of professionals, including plant scientists, agricultural
engineers, and plant breeders. This team plays a crucial role in verifying the
accuracy of PLLaMa's responses to various academic inquiries, ensuring its
effective and reliable application in the field. To support further research
and development, we have made the model's checkpoints and source codes
accessible to the scientific community. These resources are available for
download at \url{https://github.com/Xianjun-Yang/PLLaMa}.