Hyper-Align is a hypergraph-native framework that serializes high-order relations into LLM-compatible tokens via HIDT-O templates and a HIP projector, outperforming graph-centric methods on HyperAlign-Bench.
Large language models and knowledge graphs: Opportunities and challenges
6 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
VeriLLMed uses biomedical knowledge graphs to turn medical LLM reasoning into comparable paths and automatically flags three recurring error types: relation, branch, and missing errors.
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
A four-valued intensional first-order logic with a Closed Knowledge Assumption is proposed for AGI robots, treating unlisted facts as 'unknown' and inconsistent statements as the special value ⊤.
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.
citing papers explorer
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Hypergraph as Language
Hyper-Align is a hypergraph-native framework that serializes high-order relations into LLM-compatible tokens via HIDT-O templates and a HIP projector, outperforming graph-centric methods on HyperAlign-Bench.
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VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
VeriLLMed uses biomedical knowledge graphs to turn medical LLM reasoning into comparable paths and automatically flags three recurring error types: relation, branch, and missing errors.
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EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.
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Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
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Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
A four-valued intensional first-order logic with a Closed Knowledge Assumption is proposed for AGI robots, treating unlisted facts as 'unknown' and inconsistent statements as the special value ⊤.
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A Survey of Hallucination in Large Foundation Models
A survey classifying hallucination phenomena specific to large foundation models, establishing evaluation criteria, examining mitigation strategies, and discussing future directions.