  Relation extraction (RE) is a crucial task in natural language processing
(NLP) that aims to identify and classify relationships between entities
mentioned in text. In the financial domain, relation extraction plays a vital
role in extracting valuable information from financial documents, such as news
articles, earnings reports, and company filings. This paper describes our
solution to relation extraction on one such dataset REFinD. The dataset was
released along with shared task as a part of the Fourth Workshop on Knowledge
Discovery from Unstructured Data in Financial Services, co-located with SIGIR
2023. In this paper, we employed OpenAI models under the framework of
in-context learning (ICL). We utilized two retrieval strategies to find top K
relevant in-context learning demonstrations / examples from training data for a
given test example. The first retrieval mechanism, we employed, is a
learning-free dense retriever and the other system is a learning-based
retriever. We were able to achieve 3rd rank overall. Our best F1-score is
0.718.
