Integrating Knowledge Graph with Large Language Models for Multi-hop Question Generation

Large Language Model-Based Text-to-Cypher Generation for Question-Answering on Biomedical Knowledge Graphs.

Few-shot prompting heavily improves query generation quality and enables effective use of smaller models, supporting the practical deployment of locally hosted LLMs for knowledge graph question answering.

Reasoning Graph-Structured Question Answering: Datasets and Insights from LLM Benchmarking

LLMs as Parametric Knowledge Sources for Knowledge Graph Completion

This framework performs LLM knowledge elicitation to extract factual knowledge from the model’s internal representations and transforms sentence-level representations into entity-level representations and aligns them within a unified space.

KGA-LM: Representation-Level Grounding for Conversational Search over Knowledge Graphs

By decoupling knowledge injection from prompt length, the KGA-LM approach mitigates retrieval signal decay under long contexts, offering a superior trade-off between grounding quality and inference efficiency.

Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering

An LLM-judged, history-aware adaptive repair pipeline that identifies unresolved one-hop failures, continually fine-tunes on targeted repair examples, and removes or quarantines problematic noisy triples is introduced.

Diabetes question answering method based on knowledge graph and large language model

Conventional medical Question Answering (QA) systems often suffer from insufficient domain-specific knowledge grounding, limited semantic understanding, and weak answer traceability. To address these issues, this study proposes a decision-support QA framework that integrates structured retrieval over a diabetes knowled...