ArXiv

Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

2026-07-31 · 13:22 UTC ·Domen Vake, Jernej Vičič, Aleksandar Tošić ·1 min read
Authors
Domen Vake, Jernej Vičič, Aleksandar Tošić
Categories
cs.IR, cs.CL
arXiv
https://arxiv.org/abs/2607.29402v1
PDF
https://arxiv.org/pdf/2607.29402v1

Brief

Hypothetical Prompt Embeddings (HyPE) tackles the style gap between user queries and document text in Retrieval-Augmented Generation by shifting hypothetical-content generation from query time into the indexing phase: multiple synthetic prompts are generated per chunk and the chunk is embedded instead of the prompt, turning retrieval into question–question matching. Experiments on six datasets report up to +42 percentage points in retrieval precision and +45 percentage points in claim recall while introducing no runtime latency. Full paper available on arXiv; only the abstract and metadata were provided here.

Why it matters

HyPE (Hypothetical Prompt Embeddings) precomputes multiple hypothetical prompts per data chunk at indexing time and embeds the chunk in place of the prompt, converting retrieval into a question–question matching task and avoiding runtime HyDE-style synthetic-answer generation and added query latency (authors: Domen Vake, Jernej Vičič, Aleksandar Tošić).

Key details

  • On six common datasets, HyPE improved retrieval context precision by up to 42 percentage points and claim recall by up to 45 percentage points versus standard approaches; the paper is on arXiv (2607.29402v1, posted 2026-07-31) and cited as IEEE Access, vol. 13, pp. 129952–129961.
Source evidence

Abstract

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

Comment: 10 pages, 8 figures, 5 tables. Published in IEEE Access
Journal: IEEE Access, vol. 13, pp. 129952-129961, 2025