  The text retrieval is the task of retrieving similar documents to a search
query, and it is important to improve retrieval accuracy while maintaining a
certain level of retrieval speed. Existing studies have reported accuracy
improvements using language models, but many of these do not take into account
the reduction in search speed that comes with increased performance. In this
study, we propose three-stage re-ranking model using model ensembles or larger
language models to improve search accuracy while minimizing the search delay.
We ranked the documents by BM25 and language models, and then re-ranks by a
model ensemble or a larger language model for documents with high similarity to
the query. In our experiments, we train the MiniLM language model on the
MS-MARCO dataset and evaluate it in a zero-shot setting. Our proposed method
achieves higher retrieval accuracy while reducing the retrieval speed decay.
