  As online merchandise become more common, many studies focus on
embedding-based methods where queries and products are represented in the
semantic space. These methods alleviate the problem of vocab mismatch between
the language of queries and products. However, past studies usually dealt with
queries that precisely describe the product, and there still exists the need to
answer imprecise queries that may require common sense knowledge, i.e., 'what
should I get my mom for Mother's Day.' In this paper, we propose a GPT-3 based
product retrieval system that leverages the knowledge-base (KB) of GPT-3 for
question answering; users do not need to know the specific illustrative
keywords for a product when querying. Our method tunes prompt tokens of GPT-3
to prompt knowledge and render answers that are mapped directly to products
without further processing. Our method shows consistent performance improvement
on two real-world and one public dataset, compared to the baseline methods. We
provide an in-depth discussion on leveraging GPT-3 knowledge into a question
answering based retrieval system.
