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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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.
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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.