Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. Motivated by our conical distribution hypothesis, which posits that potential queries and documents form a cone-like structure in the embedding space, we introduce QAEncoder, a training-free approach to bridge this gap. Specifically, QAEncoder estimates the expectation of potential queries in the embedding space as a robust surrogate for the document embedding, and attaches document fingerprints to effectively distinguish these embeddings. Extensive experiments on fourteen embedding models across six languages and eight datasets validate QAEncoder's alignment capability, which offers a plug-and-play solution that seamlessly integrates with existing RAG architectures and training-based methods.

本研究解决了现代问答系统中用户查询与相关文档之间的匹配难题。提出的QAEncoder方法不需要训练，利用潜在查询在嵌入空间中的期望来桥接这一差距，并通过文档指纹有效区分这些嵌入。通过在多个语言和数据集上进行的广泛实验，验证了QAEncoder的对齐能力，为现有的RAG架构提供了无缝集成的解决方案。

QAEncoder：面向问答系统中的对齐表征学习