Papers Conversational Question Answering
“Conversational Question Answering” 태그가 달린 논문 156편 · 필터 해제
Adesua: Development and Feasibility Study of an AI WhatsApp Bot for Science Learning in West Africa
Sub-Saharan Africa faces persistently high student-teacher ratios and shortages of qualified teachers, limiting students' access to personalized learning support and formative assessment. To address this challenge, we pr…
Conversational Question AnsweringComprehensive Comparison of RAG Methods Across Multi-Domain Conversational QA
Conversational question answering increasingly relies on retrieval-augmented generation (RAG) to ground large language models (LLMs) in external knowledge. Yet, most existing studies evaluate RAG methods in isolation and…
Conversational Question AnsweringAnswer GenerationPlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or task-agnostic but less effective due to…
Conversational Question AnsweringBenchmarking Contextual Understanding for In-Car Conversational Systems
In-Car Conversational Question Answering (ConvQA) systems significantly enhance user experience by enabling seamless voice interactions. However, assessing their accuracy and reliability remains a challenge. This paper e…
Conversational Question AnsweringSEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches often suf…
Conversational Question AnsweringComputational EfficiencyLogical ReasoningKnowledge GraphsChatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs
Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in enterprise and domain applications to pr…
Conversational Question AnsweringNatural QuestionsKnowledge GraphsReasoning about Intent for Ambiguous Requests
Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safety risks when that interpretation is wrong. We propose generating a single stru…
Conversational Question AnsweringReinforcement LearningSemantic ParsingChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering
We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an important role in CQA, where user intent evolves across dialogue turns, and ut…
Conversational Question AnsweringReinforcement LearningBeyond Stars: Bridging the Gap Between Ratings and Review Sentiment with LLM
We present an advanced approach to mobile app review analysis aimed at addressing limitations inherent in traditional star-rating systems. Star ratings, although intuitive and popular among users, often fail to capture t…
Conversational Question AnsweringSGMem: Sentence Graph Memory for Long-Term Conversational Agents
Long-term conversational agents require effective memory management to handle dialogue histories that exceed the context window of large language models (LLMs). Existing methods based on fact extraction or summarization …
Conversational Question AnsweringResponse GenerationEpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Key-Value (KV) cache grows linearly with t…
Conversational Question AnsweringTowards Adaptive Context Management for Intelligent Conversational Question Answering
This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework is to optimize the use of the conversatio…
Conversational Question AnsweringA Survey of the State-of-the-Art in Conversational Question Answering Systems
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…
Conversational Question AnsweringReinforcement LearningContrastive LearningTransfer LearningBuilding Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
We consider open-retrieval conversational question answering (OR-CONVQA), an extension of question answering where system responses need to be (i) aware of dialog history and (ii) grounded in documents (or document fragm…
Conversational Question AnsweringDisambiguation in Conversational Question Answering in the Era of LLM: A Survey
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language Models (LLMs), addressing ambiguity has b…
BenchmarkingConversational Question AnsweringQuestion AnsweringBottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts
Training conversational question-answering (QA) systems requires a substantial amount of in-domain data, which is often scarce in practice. A common solution to this challenge is to generate synthetic data. Traditional m…
Conversational Question AnsweringLanguage ModelingLanguage ModellingLarge Language Model+1Automated Factual Benchmarking for In-Car Conversational Systems using Large Language Models
In-car conversational systems bring the promise to improve the in-vehicle user experience. Modern conversational systems are based on Large Language Models (LLMs), which makes them prone to errors such as hallucinations,…
BenchmarkingConversational Question AnsweringQuestion AnsweringPreference-based Learning with Retrieval Augmented Generation for Conversational Question Answering
Conversational Question Answering (ConvQA) involves multiple subtasks, i) to understand incomplete questions in their context, ii) to retrieve relevant information, and iii) to generate answers. This work presents PRAISE…
Conversational Question AnsweringQuestion AnsweringRetrievalRetrieval-augmented GenerationFew-shot Policy (de)composition in Conversational Question Answering
The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions…
Conversational Question AnsweringQuestion AnsweringReading ComprehensionRAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has …
Conversational Question AnsweringKnowledge GraphsQuestion AnsweringRAG+2