Recent advances in language models (LMs) has driven progress in information retrieval (IR), effectively extracting semantically relevant information. However, they face challenges in balancing computational costs with deeper query-document interactions. To tackle this, we present two mechanisms: 1) a light and effective multi-vector retrieval with sequence compression vectors, dubbed SCV and 2) coarse-to-fine vector search. The strengths of SCV stems from its application of span compressive vectors for scoring. By employing a non-linear operation to examine every token in the document, we abstract these into a span-level representation. These vectors effectively reduce the document’s dimensional representation, enabling the model to engage comprehensively with tokens across the entire collection of documents, rather than the subset retrieved by Approximate Nearest Neighbor. Therefore, our framework performs a coarse single vector search during the inference stage and conducts a fine-grained multi-vector search end-to-end. This approach effectively reduces the cost required for search. We empirically show that SCV achieves the fastest latency compared to other state-of-the-art models and can obtain competitive performance on both in-domain and out-of-domain benchmark datasets.

Retrieval-augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the parametric knowledge of large language models (LLMs). While previous approaches focused on processing retrieved passages to remove irrelevant context, they still rely heavily on the quality of retrieved passages which can degrade if the question is ambiguous or complex. In this paper, we propose a simple yet efficient method called question and passage augmentation (QPaug) via LLMs for open-domain QA. QPaug first decomposes the original questions into multiple-step sub-questions. By augmenting the original question with detailed sub-questions and planning, we are able to make the query more specific on what needs to be retrieved, improving the retrieval performance. In addition, to compensate for the case where the retrieved passages contain distracting information or divided opinions, we augment the retrieved passages with self-generated passages by LLMs to guide the answer extraction. Experimental results show that QPaug outperforms the previous state-of-the-art and achieves significant performance gain over existing RAG methods.