mlsanityData to 7 Oct 2026

Conversational Question Answering: A Survey

arXiv 2106.00874 ↗First posted 2 Jun 2021cs.CL cs.AI cs.IRcited by 31

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Abstract (arXiv)

Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on Conversational Question Answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy the user's information needs. Whilst the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey, therefore, is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 79 of its 122 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

79 of 122 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 14; 2024: 6; 2025: 2; 2026: 1.

31 papers.

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  1. 2022ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answeringcited by 194
  2. 2023A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4cited by 81
  3. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  4. 2023Large Language Models are Visual Reasoning Coordinatorscited by 58
  5. 2023AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMscited by 21
  6. 2022LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphscited by 15
  7. 2022Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunitiescited by 14
  8. 2023Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answeringcited by 14
  9. 2024Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Factscited by 14
  10. 2024ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decodingcited by 12
  11. 2023CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answeringcited by 10
  12. 2021More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answeringcited by 8
19 more
  1. 2023Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospectscited by 6
  2. 2023Knowledge Graphs Queryingcited by 5
  3. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
  4. 2023Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Reviewcited by 4
  5. 2023Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answeringcited by 3
  6. 2026Hippocampus: An Efficient and Scalable Memory Module for Agentic AIcited by 3
  7. 2022Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphscited by 2
  8. 2023Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learningcited by 2
  9. 2022A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deceptioncited by 1
  10. 2023Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehensioncited by 1
  11. 2024Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processingcited by 1
  12. 2024Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Modelscited by 1
  13. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  14. 2021Recent Advances in Automated Question Answering In Biomedical Domaincited by 0
  15. 2022Towards leveraging latent knowledge and Dialogue context for real-world conversational question answeringcited by 0
  16. 2023OpinionConv: Conversational Product Search with Grounded Opinionscited by 0
  17. 2024Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulationcited by 0
  18. 2024PerkwE_COQA: Enhanced Persian Conversational Question Answering by combining contextual keyword extraction with Large Language Modelscited by 0
  19. 2025Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Modelscited by 0