mlsanityData to 7 Oct 2026

QAEncoder: Towards Aligned Representation Learning in Question Answering Systems

arXiv 2409.20434 ↗First posted 30 Sep 2024cs.CLcited by 3

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

Abstract (arXiv)

Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. We introduce QAEncoder, a training-free approach to bridge this gap. Specifically, QAEncoder estimates the expectation of potential queries in the embedding space as a robust surrogate for the document embedding, and attaches document fingerprints to effectively distinguish these embeddings. Extensive experiments across diverse datasets, languages, and embedding models confirmed QAEncoder's alignment capability, which offers a simple-yet-effective solution with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues. The repository is publicly available at https://github.com/IAAR-Shanghai/QAEncoder.

Key facts. Cited by 3 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 47 of its 82 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

47 of 82 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct
  2. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference4 Oct

Other references · 45

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  10. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  11. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  12. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
33 more
  1. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  4. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  5. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  6. 2019ELI5: Long Form Question Answeringcited by 567
  7. 2020Fact or Fiction: Verifying Scientific Claimscited by 546
  8. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  9. 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 426
  10. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 410
  11. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 357
  12. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  13. 2021Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Samplingcited by 238
  14. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 233
  15. 2019Document Expansion by Query Predictioncited by 225
  16. 2021PAQ: 65 Million Probably-Asked Questions and What You Can Do With Themcited by 198
  17. 2020CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claimscited by 195
  18. 2021GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrievalcited by 119
  19. 2023Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documentscited by 103
  20. 2020Entities as Experts: Sparse Memory Access with Entity Supervisioncited by 100
  21. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 95
  22. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 80
  23. 2021Learning Passage Impacts for Inverted Indexescited by 68
  24. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  25. 2020BERT-QE: Contextualized Query Expansion for Document Re-rankingcited by 40
  26. 2022Exploring Dual Encoder Architectures for Question Answeringcited by 19
  27. 2022Query-as-context Pre-training for Dense Passage Retrievalcited by 9
  28. 2024Tabular Embedding Model (TEM): Finetuning Embedding Models For Tabular RAG Applicationscited by 8
  29. 2024The FIGNEWS Shared Task on News Media Narrativescited by 7
  30. 2023QUADRo: Dataset and Models for QUestion-Answer Database Retrievalcited by 5
  31. 2022Learning Diverse Document Representations with Deep Query Interactions for Dense Retrievalcited by 4
  32. 2022Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gapscited by 2
  33. 2024Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuningcited by 2

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