A Gaussian information-gain metric in embedding space quantifies semantic progress in dialogues via uncertainty reduction and shows competitive agreement with human judgments on MT-Bench and UltraFeedback.
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
KG-Guard augments knowledge graphs with a virtual question node and uses a graph encoder plus MLP to classify LLM-proposed answers as hallucinations or not, reporting superior F1 scores and downstream improvements on three benchmarks.
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.
citing papers explorer
-
Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
-
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
GS-Quant generates coarse-to-fine discrete codes for KG entities via semantic hierarchy injection and causal sequence reconstruction, enabling LLMs to perform knowledge graph completion by treating the codes as vocabulary tokens.