Develops a theoretical perspective showing no hard rule can perfectly reject false unsupported trajectories while retaining true-but-unobserved ones under incomplete graph evidence, and characterizes soft grounding as KL-regularized deformation of the LLM prior.
QA - GNN : Reasoning with Language Models and Knowledge Graphs for Question Answering
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
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.
Hybrid sparse-dense retrieval achieves Hit@5 of 0.917 on a new curated benchmark of silicon detector papers with released code and annotations.
citing papers explorer
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Grounding LLM Reasoning under Incomplete Graph Evidence
Develops a theoretical perspective showing no hard rule can perfectly reject false unsupported trajectories while retaining true-but-unobserved ones under incomplete graph evidence, and characterizes soft grounding as KL-regularized deformation of the LLM prior.
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KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
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.
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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.
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A Reproducible Benchmark and Evidence-Retrieval Software Framework for Silicon Detector R&D Literature
Hybrid sparse-dense retrieval achieves Hit@5 of 0.917 on a new curated benchmark of silicon detector papers with released code and annotations.