mlsanityData to 7 Oct 2026

QuOTE: Question-Oriented Text Embeddings

arXiv 2502.10976 ↗First posted 16 Feb 2025cs.IR cs.AI cs.CLcited by 2

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 38 references that are in the map. First posted on arXiv 16 Feb 2025.

Builds on

24 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering6 Oct

Other references · 23

  1. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  2. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  3. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  4. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  5. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  6. 2019ELI5: Long Form Question Answeringcited by 567
  7. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  8. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  9. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
  10. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  11. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 253
  12. 2021Simple Entity-Centric Questions Challenge Dense Retrieverscited by 140
11 more
  1. 2024xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Tokencited by 123
  2. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 104
  3. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 70
  4. 2022Multi-hop Question Answeringcited by 64
  5. 2021Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?cited by 59
  6. 2021Dense Hierarchical Retrieval for Open-Domain Question Answeringcited by 33
  7. 2024Dense Passage Retrieval: Is it Retrieving?cited by 12
  8. 2024A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judgecited by 9
  9. 2024Drowning in Documents: Consequences of Scaling Reranker Inferencecited by 8
  10. 2017A Frame Tracking Model for Memory-Enhanced Dialogue Systemscited by 4
  11. 2022Ask to Understand: Question Generation for Multi-hop Question Answeringcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generationcited by 0
  2. 2026IUU+DB: Tracking Illegal, Unreported, and Unregulated Fishing, Seafood Fraud, and Labor Abuse through LLM-driven Information Extractioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Transforming Questions and Documents for Semantically Aligned Retrieval-Augmented Generation
  2. 2026Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings
  3. 2025ELITE: Embedding-Less retrieval with Iterative Text Exploration
  4. 2025Question Decomposition for Retrieval-Augmented Generation
  5. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
  6. 2025Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking by Contrasting Layers
  7. 2026Comparing Chunking and Embedding Strategies for Turkish RAG Systems
  8. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions