mlsanityData to 14 Oct 2026

TopiOCQA: Open-domain Conversational Question Answering with Topic Switching

arXiv 2110.00768 ↗First posted 2 Oct 2021cs.CLcited by 96

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Cited by 21 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, i.e., the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best model achieves F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of our dataset. Our dataset and code is available at https://mcgill-nlp.github.io/topiocqa

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). Builds on 29 of its 43 references that are in the map. First posted on arXiv 2 Oct 2021.

Builds on

29 of 43 references.

References · 29

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2019Language Models as Knowledge Bases?cited by 1,936
  8. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  9. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  10. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
  11. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  12. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 719
17 more
  1. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 695
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  3. 2018CoQA: A Conversational Question Answering Challengecited by 683
  4. 2017The NarrativeQA Reading Comprehension Challengecited by 597
  5. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
  6. 2018QuAC : Question Answering in Contextcited by 381
  7. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  8. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 148
  9. 2014Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)cited by 109
  10. 2020Open-Retrieval Conversational Question Answeringcited by 99
  11. 2019Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answeringcited by 96
  12. 2020Question Rewriting for Conversational Question Answeringcited by 93
  13. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  14. 2019BERT with History Answer Embedding for Conversational Question Answeringcited by 67
  15. 2020TREC CAsT 2019: The Conversational Assistance Track Overviewcited by 64
  16. 2019Attentive History Selection for Conversational Question Answeringcited by 33
  17. 2018SDNet: Contextualized Attention-based Deep Network for Conversational Question Answeringcited by 30

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 18; 2024: 32; 2025: 23; 2026: 17.

96 papers.

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  1. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  2. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 201
  3. 2024WebLINX: Real-World Website Navigation with Multi-Turn Dialoguecited by 180
  4. 2023Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answeringcited by 151
  5. 2023The Generative AI Paradox: "What It Can Create, It May Not Understand"cited by 98
  6. 2023MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversationcited by 97
  7. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  8. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  9. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  10. 2023ConvGQR: Generative Query Reformulation for Conversational Searchcited by 59
  11. 2024Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?cited by 56
  12. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMscited by 42
38 more
  1. 2024Knowledge Boundary of Large Language Models: A Surveycited by 40
  2. 2023PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adaptercited by 38
  3. 2025Search Arena: Analyzing Search-Augmented LLMscited by 34
  4. 2024CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Searchcited by 29
  5. 2022INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactionscited by 27
  6. 2023The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answeringcited by 26
  7. 2023Learning to Relate to Previous Turns in Conversational Searchcited by 26
  8. 2023Personalized Large Language Model Assistant with Evolving Conditional Memorycited by 25
  9. 2024CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systemscited by 25
  10. 2024Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Checkcited by 24
  11. 2024Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversationcited by 23
  12. 2024A Survey on Recent Advances in Conversational Data Generationcited by 23
  13. 2024A Survey of Conversational Searchcited by 22
  14. 2025A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applicationscited by 22
  15. 2021Ditch the Gold Standard: Re-evaluating Conversational Question Answeringcited by 19
  16. 2022Saving Dense Retriever from Shortcut Dependency in Conversational Searchcited by 19
  17. 2024ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrievalcited by 19
  18. 2024Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Actioncited by 19
  19. 2024Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentationcited by 17
  20. 2024Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systemscited by 15
  21. 2024AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignmentcited by 14
  22. 2022On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Methodcited by 12
  23. 2024XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inferencecited by 12
  24. 2025UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversationscited by 12
  25. 2024Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Searchcited by 11
  26. 2023textitDial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuningcited by 9
  27. 2024Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversationscited by 9
  28. 2024Assessing "Implicit" Retrieval Robustness of Large Language Modelscited by 9
  29. 2024Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QAcited by 9
  30. 2024DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Searchcited by 9
  31. 2025EnronQA: Towards Personalized RAG over Private Documentscited by 9
  32. 2025REFRAG: Rethinking RAG based Decodingcited by 9
  33. 2022Open-Domain Conversational Question Answering with Historical Answerscited by 8
  34. 2024ConvSDG: Session Data Generation for Conversational Searchcited by 8
  35. 2024CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generationcited by 8
  36. 2025Conversational Search: From Fundamentals to Frontiers in the LLM Eracited by 8
  37. 2024Beyond Persuasion: Towards Conversational Recommender System with Credible Explanationscited by 7
  38. 2025Multi-Agent LLM Judge: automatic personalized LLM judge design for evaluating natural language generation applicationscited by 7

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