mlsanityData to 7 Oct 2026

Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

arXiv 2101.00774 ↗First posted 4 Jan 2021cs.AIcited by 178

Not on the map. Why?

Cited by 37 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC). While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QA systems. In this work, we review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. Specifically, we begin with revisiting the origin and development of OpenQA systems. We then introduce modern OpenQA architecture named "Retriever-Reader" and analyze the various systems that follow this architecture as well as the specific techniques adopted in each of the components. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used. We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field.

Key facts. Cited by 178 papers in the map (data through 7 Oct 2026). Builds on 90 of its 163 references that are in the map. First posted on arXiv 4 Jan 2021.

Builds on

90 of 163 references.

References · 90

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  8. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  9. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  10. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  11. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  12. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
38 more
  1. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  2. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  3. 2017Billion-scale similarity search with GPUscited by 2,477
  4. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,140
  5. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  6. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,114
  7. 2019Language Models as Knowledge Bases?cited by 1,922
  8. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  9. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  10. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
  11. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  12. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,160
  13. 2015Teaching Machines to Read and Comprehendcited by 1,039
  14. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 883
  15. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  16. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
  17. 2015Gated Graph Sequence Neural Networkscited by 792
  18. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  19. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  20. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
  21. 2018CoQA: A Conversational Question Answering Challengecited by 674
  22. 2017The NarrativeQA Reading Comprehension Challengecited by 590
  23. 2019ELI5: Long Form Question Answeringcited by 567
  24. 2017Supervised Learning of Universal Sentence Representations from Natural Language Inference Datacited by 428
  25. 2018QuAC : Question Answering in Contextcited by 379
  26. 2017A simple neural network module for relational reasoningcited by 345
  27. 2018SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inferencecited by 333
  28. 2016NewsQA: A Machine Comprehension Datasetcited by 330
  29. 2019Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoningcited by 281
  30. 2016Bidirectional Attention Flow for Machine Comprehensioncited by 278
  31. 2017SearchQA: A New Q&A Dataset Augmented with Context from a Search Enginecited by 221
  32. 2018ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehensioncited by 198
  33. 2017Constructing Datasets for Multi-hop Reading Comprehension Across Documentscited by 190
  34. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  35. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  36. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  37. 2018Open Domain Question Answering Using Early Fusion of Knowledge Bases and Textcited by 167
  38. 2018QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehensioncited by 164

50 most cited of 90 All 90 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 38; 2023: 36; 2024: 57; 2025: 19; 2026: 10.

178 papers.

Sort:

By area:

  1. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  2. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  3. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  4. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  5. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  6. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  7. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 204
  8. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  9. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  10. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 150
  11. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  12. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGcited by 122
38 more
  1. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  2. 2024M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understandingcited by 110
  3. 2023LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunitiescited by 97
  4. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  5. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  6. 2022Towards Complex Document Understanding By Discrete Reasoningcited by 82
  7. 2024LLMs are Good Sign Language Translatorscited by 76
  8. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 71
  9. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 69
  10. 2023Graph Neural Prompting with Large Language Modelscited by 64
  11. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  12. 2022I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classificationcited by 54
  13. 2021MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documentscited by 53
  14. 2024Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?cited by 47
  15. 2022Retrieval-Enhanced Machine Learningcited by 46
  16. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 46
  17. 2023ToolTalk: Evaluating Tool-Usage in a Conversational Settingcited by 42
  18. 2022A Decade of Knowledge Graphs in Natural Language Processing: A Surveycited by 40
  19. 2022Self-Prompting Large Language Models for Zero-Shot Open-Domain QAcited by 39
  20. 2023Evaluating Open-QA Evaluationcited by 35
  21. 2023How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advancescited by 33
  22. 2023Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraintscited by 31
  23. 2024PEDANTS: Cheap but Effective and Interpretable Answer Equivalencecited by 31
  24. 2021Challenges in Generalization in Open Domain Question Answeringcited by 30
  25. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  26. 2023KnowGPT: Knowledge Graph based Prompting for Large Language Modelscited by 30
  27. 2024Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximizationcited by 29
  28. 2024REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answeringcited by 28
  29. 2024TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systemscited by 28
  30. 2022Chart Question Answering: State of the Art and Future Directionscited by 26
  31. 2023Large Language Models are Built-in Autoregressive Search Enginescited by 26
  32. 2023IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositionscited by 25
  33. 2023HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responsescited by 25
  34. 2024Making Long-Context Language Models Better Multi-Hop Reasonerscited by 25
  35. 2022Visconde: Multi-document QA with GPT-3 and Neural Rerankingcited by 23
  36. 2024Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Modelscited by 23
  37. 2022Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answeringcited by 22
  38. 2024CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAGcited by 22

50 most cited of 178 All 178 (JSON)