Ontology-Based Prompt Engineering with Large Language Models

Abstract

Large language models have become a widely discussed topic that have a variety of potential applications which can be used by people with little technical knowledge and no coding skills. One of the potential applications is using large language models for information extraction tasks, such as for example when one wants to extract data from tables that are available in PDF files. This is very common in the financial industry, where data from annual reports needs to be gathered and made available for further analysis. However, large language models tend to hallucinate and provide wrong information in some cases, which creates a need for improved accuracy. A common strategy to improve the accuracy is with prompt engineering, which allows to influence the results of large language models by changing the input to the large language model.