QuOTE: Question-Oriented Text Embeddings

TL;DR

QuOTE introduces question-augmented text embeddings to improve document retrieval accuracy in RAG systems by aligning representations with query semantics through hypothetical questions.

Contribution

It proposes a novel question-oriented embedding method that enhances document representation for retrieval-augmented generation systems.

Findings

Significantly improves retrieval accuracy across benchmarks

Enhances performance in multi-hop question-answering tasks

Demonstrates versatility of question generation in indexing

Abstract

We present QuOTE (Question-Oriented Text Embeddings), a novel enhancement to retrieval-augmented generation (RAG) systems, aimed at improving document representation for accurate and nuanced retrieval. Unlike traditional RAG pipelines, which rely on embedding raw text chunks, QuOTE augments chunks with hypothetical questions that the chunk can potentially answer, enriching the representation space. This better aligns document embeddings with user query semantics, and helps address issues such as ambiguity and context-dependent relevance. Through extensive experiments across diverse benchmarks, we demonstrate that QuOTE significantly enhances retrieval accuracy, including in multi-hop question-answering tasks. Our findings highlight the versatility of question generation as a fundamental indexing strategy, opening new avenues for integrating question generation into retrieval-based AI pipelines.