Distilling Named Entity Recognition Models for Endangered Species from Large Language Models

arXiv:2403.15430v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl data datasets data sources domain endangered species experts knowledge language language models language processing large language large language models llm natural natural language natural language processing nlp papers patents processing recognition searching species type unstructured unstructured data