将假设答案生成提前到索引阶段，提升问答匹配精度。
Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings
- 在索引时预生成多个假设提示并嵌入文档
- 检索精度提升最高达42个百分点，召回率提升45个百分点
- 兼容重排序等RAG技术，无额外查询延迟
检索增强生成（RAG）系统通过结合检索机制与生成式语言模型，提升回答的准确性和相关性。然而，用户查询与文档文本之间的风格差异仍是持续挑战，现有运行时方案（如假设文档嵌入，HyDE）虽尝试改善对齐，却引入额外计算开销。为此，我们提出假设提示嵌入（HyPE），将假设内容生成从查询阶段移至索引阶段。通过为每个数据块预先生成多个假设提示，并以提示嵌入替代原始文本，HyPE将检索转化为问题-问题匹配任务，避免运行时合成答案生成，不增加延迟的同时强化查询与上下文对齐。在六个常见数据集上的实验表明，相比标准方法，HyPE可使检索上下文精确度提升最高达42个百分点，召回率提升最高达45个百分点，且兼容重排序、多向量检索、查询分解等RAG先进方法。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text remains a persistent challenge in retrieval-augmented systems, often addressed by runtime solutions (e.g., Hypothetical Document Embeddings (HyDE)) that attempt to improve alignment but introduce extra computational overhead at query time. To address these challenges, we propose Hypothetical Prompt Embeddings (HyPE), a framework that shifts the generation of hypothetical content from query time to the indexing phase. By precomputing multiple hypothetical prompts for each data chunk and embedding the chunk in place of the prompt, HyPE transforms retrieval into a question-question matching task, bypassing the need for runtime synthetic answer generation. This approach does not introduce latency but also strengthens the alignment between queries and relevant context. Our experimental results on six common datasets show that HyPE can improve retrieval context precision by up to 42 percentage points and claim recall by up to 45 percentage points, compared to standard approaches, while remaining compatible with re-ranking, multi-vector retrieval, query decomposition, and other RAG advancements
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