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.

翻译：文本检索是从搜索查询中检索相似文档的任务，在保持一定检索速度的同时提升检索准确率至关重要。现有研究通过语言模型提升了检索准确率，但多数未考虑性能提升带来的搜索速度下降问题。本研究提出一种基于模型集成或更大语言模型的三阶段重排序方法，在最小化搜索延迟的同时提升搜索准确率。我们首先通过BM25和语言模型对文档进行排序，随后针对与查询高度相似的文档，采用模型集成或更大语言模型进行重排序。实验基于MS-MARCO数据集训练MiniLM语言模型，并在零样本设置下进行评估。结果表明，所提方法在降低检索速度衰减的同时实现了更高的检索准确率。