In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous queries, making it challenging for the retriever to find relevant passages quickly. This paper introduces Hybrid Text Generation-Based Query Expansion (HTGQE), an effective method to improve retrieval efficiency. HTGQE combines large language models with Pseudo-Relevance Feedback techniques to enhance the input for generative models, improving text generation speed and quality. Building on this foundation, HTGQE employs multiple query expansion generators, each trained to provide query expansion contexts from distinct perspectives. This enables the retriever to explore relevant passages from various angles for complementary retrieval results. As a result, under an extractive and generative QA setup, HTGQE achieves promising results on both Natural Questions (NQ) and TriviaQA (Trivia) datasets for passage retrieval and reading tasks.

In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and passage sides are equally important and should be considered for improved OpenQA performance. In this paper, we propose a contrastive pseudo-labeled data constructed around passages and queries separately. We employ an improved pseudo-relevance feedback (PRF) algorithm with a knowledge-filtering strategy to enrich the semantic information in dense representations. Additionally, we proposed an Auto Text Representation Optimization Model (AOpt) to iteratively update the dense representations. Experimental results demonstrate that our methods effectively optimize dense representations, making them more distinguishable in dense retrieval, thus improving the OpenQA system’s overall performance.