Knowledge Tree
conversational question answering
Also known as: conversational question-answering, conversational question answering systems
Facts (10)
Sources
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 9 facts
referenceLiu et al. (2024b) developed a method for conversational question answering using language model-generated reformulations over knowledge graphs.
claimDeveloping conversational Question Answering with retrieval strategies can dynamically detect and adjust knowledge biases and improve the explainability of Retrieval-Augmented Generation (RAG) systems through multi-turn user interactions.
referenceJain and Lapata introduced a knowledge aggregation module and graph reasoning to facilitate joint reasoning between knowledge graphs and large language models for conversational question-answering.
claimRAG (Roy et al., 2024) and KG-RAG (Sanmartin, 2024) improve LLM capabilities in understanding user interactions to generate accurate answers for conversational Question Answering.
claimThe combination of knowledge fusion, Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) reasoning, and ranking-based refinement accelerates complex question decomposition for multi-hop Question Answering, enhances context understanding for conversational Question Answering, facilitates cross-modal interactions for multi-modal Question Answering, and improves the explainability of generated answers.
claimApproaches that leverage retrieved factual evidence from knowledge graphs for refinement and validation are designed to augment Large Language Model capabilities in understanding user interactions and verifying intermediate reasoning for multi-hop question-answering (Chen et al., 2024b) and conversational question-answering (Xiong et al., 2024).
referenceSELF-multi-RAG leverages large language models to retrieve information from summarized conversational history and reuses that retrieved knowledge for augmentation to improve contextual understanding and answer quality in conversational question-answering.
referenceRoy et al. (2024) published 'Learning when to retrieve, what to rewrite, and how to respond in conversational QA' in EMNLP, pages 10604–10625, focusing on decision-making processes in conversational question answering.
claimConversational Question Answering involves user engagement in multi-turn interactions to understand the given context, determine final answers, and satisfy information needs.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 1 fact
referenceChatKBQA (Luo H. et al., 2023) and RoG (Luo et al., 2023b) integrate knowledge graph reasoning into conversational question answering systems to enhance factual accuracy and discourse coherence.