Information retrieval models with GPT-3 : techniques for improving ranking performance through text enhancement

![CDATA[This paper discusses techniques for improving the ranking performance of information retrieval models through text enhancement using GPT-3’s Large Language Model (LLM). Our goal is to demonstrate how the relevance of retrieved documents can be improved by ingesting and indexing better quality corpus data in the Solr search engine. We describe the methodology used in our research and present an analysis and evaluation of our test results. Our conclusion is that using GPT-3 to generate higher quality documents can enhance the relevance of retrieved documents in information retrieval models. This provides another alternative for evaluating retrieval models using test collections made available to the retrieval research community at large.]]