Papers Knowledge Base Question Answering
“Knowledge Base Question Answering” 태그가 달린 논문 148편 · 필터 해제
SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form. We study the latter para…
Knowledge Base Question AnsweringInformation RetrievalSemantic ParsingDeSQ: Decomposition-based SPARQL Query Generation
Dominant approaches to Knowledge Base Question Answering (KBQA) fall into two categories. First is the generation of a formal query that suffers from brittleness and limited explainability, and the second is direct answe…
Knowledge Base Question AnsweringKG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-graph subgraph. Yet LLMs are known to halluc…
Knowledge Base Question AnsweringNode ClassificationGAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe knowledge-base feedback, and eventually return an answer. However, c…
Knowledge Base Question AnsweringReinforcement LearningDo Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Explicit planning is a critical capability for LLM-based agents solving complex data-centric tasks, which require precise tool calling over external data sources. Existing strategies fall into two paradigms based on plan…
Knowledge Base Question AnsweringKG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive reasoning. Structured Knowledge Graphs (KGs) provide an effective form of…
Knowledge Base Question AnsweringNatural Language UnderstandingReinforcement LearningKnowledge GraphsTopology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question Answering (KBQA) mitigates this by groundi…
Knowledge Base Question AnsweringGraph Neural NetworkAnswer GenerationKnowledge GraphsJoint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models
Knowledge Bases (KBs) play a key role in various applications. As two representative KB-related tasks, knowledge base completion (KBC) and knowledge base question answering (KBQA) are closely related and inherently compl…
Knowledge Base Question AnsweringKG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KBQA), which leverages structured Knowledg…
Knowledge Base Question AnsweringReinforcement LearningKnowledge GraphsKBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering
Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generating executable logical forms. While Large Language Models (LLMs) have adv…
Knowledge Base Question AnsweringReinforcement LearningKnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA
Knowledge Base Question Answering (KBQA) aims to answer natural-language questions over a structured Knowledge Base (KB). Recent work improves KBQA by adopting an agentic reasoning paradigm, in which Large Language Model…
Knowledge Base Question AnsweringReinforcement LearningTextlessRAG: End-to-End Visual Document RAG by Speech Without Text
Document images encapsulate a wealth of knowledge, while the portability of spoken queries enables broader and flexible application scenarios. Yet, no prior work has explored knowledge base question answering over visual…
Knowledge Base Question AnsweringiQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly kno…
Graph Neural NetworkKnowledge Base Question AnsweringKnowledge GraphsQuestion AnsweringBeyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering
Knowledge Base Question Answering (KBQA) aims to answer natural language questions using structured knowledge from KBs. While LLM-only approaches offer generalization, they suffer from outdated knowledge, hallucinations,…
Knowledge Base Question AnsweringQuestion AnsweringRAGSemantic ParsingPrivate LoRA Fine-tuning of Open-Source LLMs with Homomorphic Encryption
Preserving data confidentiality during the fine-tuning of open-source Large Language Models (LLMs) is crucial for sensitive applications. This work introduces an interactive protocol adapting the Low-Rank Adaptation (LoR…
GPUKnowledge Base Question AnsweringQuantizationQuestion AnsweringMCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering
This study explores how to enhance the reasoning capabilities of large language models (LLMs) in knowledge base question answering (KBQA) by leveraging Monte Carlo Tree Search (MCTS). Semantic parsing-based KBQA methods …
Decision MakingKnowledge Base Question AnsweringQuestion AnsweringSemantic ParsingBeyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation
Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements at test time…
Entity RetrievalFormKnowledge Base Question AnsweringQuestion Answering+1KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language models (LLMs), KBQA still faces challenges in…
Heuristic SearchKnowledge Base Question AnsweringQuestion AnsweringTARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data
Semantic parsing, which converts natural language questions into logic forms, plays a crucial role in reasoning within structured environments. However, existing methods encounter two significant challenges: reliance on …
In-Context LearningKnowledge Base Question AnsweringQuestion AnsweringSemantic Parsing+1CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024…
Knowledge Base Question AnsweringKnowledge GraphsQuestion AnsweringRetrieval+1