Time-Sensitve Retrieval-Augmented Generation for Question Answering

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by accessing external data sources, offering a promising way to improve accuracy and reliability. Despite its potential, conventional retrievers encounter bias and flaws with time-sensitive queries. In this paper, a benchmark query dataset is constructed to retrieve documents containing time-evolving facts, and the results show that current embedding-based similarity-matching methods struggle to handle queries with explicit temporal constraints. Therefore, we propose a novel approach that integrates supervised contrastive learning with tailored negative sample pairs for temporal constraints to train the retriever of an RAG system, along with query-side fine-tuning and routing techniques. Experimental results show that our approach significantly enhances the retriever performance of time-sensitive queries while ensuring the effectiveness of general queries. We will make the code and dataset publicly available at https://github.com/suzhou-22/TS-Retriever.