  Natural language processing (NLP) practitioners are leveraging large language
models (LLM) to create structured datasets from semi-structured and
unstructured data sources such as patents, papers, and theses, without having
domain-specific knowledge. At the same time, ecological experts are searching
for a variety of means to preserve biodiversity. To contribute to these
efforts, we focused on endangered species and through in-context learning, we
distilled knowledge from GPT-4. In effect, we created datasets for both named
entity recognition (NER) and relation extraction (RE) via a two-stage process:
1) we generated synthetic data from GPT-4 of four classes of endangered
species, 2) humans verified the factual accuracy of the synthetic data,
resulting in gold data. Eventually, our novel dataset contains a total of 3.6K
sentences, evenly divided between 1.8K NER and 1.8K RE sentences. The
constructed dataset was then used to fine-tune both general BERT and
domain-specific BERT variants, completing the knowledge distillation process
from GPT-4 to BERT, because GPT-4 is resource intensive. Experiments show that
our knowledge transfer approach is effective at creating a NER model suitable
for detecting endangered species from texts.
