mlsanityData to 14 Oct 2026

A Survey of the State-of-the-Art in Conversational Question Answering Systems

arXiv 2509.05716 ↗First posted 6 Sep 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

Conversational Question Answering (ConvQA) systems have emerged as a pivotal area within Natural Language Processing (NLP) by driving advancements that enable machines to engage in dynamic and context-aware conversations. These capabilities are increasingly being applied across various domains, i.e., customer support, education, legal, and healthcare where maintaining a coherent and relevant conversation is essential. Building on recent advancements, this survey provides a comprehensive analysis of the state-of-the-art in ConvQA. This survey begins by examining the core components of ConvQA systems, i.e., history selection, question understanding, and answer prediction, highlighting their interplay in ensuring coherence and relevance in multi-turn conversations. It further investigates the use of advanced machine learning techniques, including but not limited to, reinforcement learning, contrastive learning, and transfer learning to improve ConvQA accuracy and efficiency. The pivotal role of large language models, i.e., RoBERTa, GPT-4, Gemini 2.0 Flash, Mistral 7B, and LLaMA 3, is also explored, thereby showcasing their impact through data scalability and architectural advancements. Additionally, this survey presents a comprehensive analysis of key ConvQA datasets and concludes by outlining open research directions. Overall, this work offers a comprehensive overview of the ConvQA landscape and provides valuable insights to guide future advancements in the field.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 78 of its 142 references that are in the map. First posted on arXiv 6 Sep 2025.

Builds on

78 of 142 references.

References · 78

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38 more
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  1. 2026When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Informationcited by 0

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  6. 2022PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance
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