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

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

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-a...

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Bibliographic Details
Main Authors: Domen Vake, Jernej Vicic, Aleksandar Tosic
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Dense retrieval
embeddings
hypothetical prompt embeddings
large language models
retrieval-augmented generation
Online Access:https://ieeexplore.ieee.org/document/11080443/
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