Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework

arXiv:2609.30484 · cs.AI, cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/aaivu/knowledgextraction

Terminology

• AUROC — 33× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Knowledge Graph — 11× in this paper · explained in 7 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Contextual Understanding — 8× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• F1 Score — 6× in this paper · explained in 3 episodes
• AUC — 5× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Semantic Similarity — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Contextual Understanding Score — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Ontology — 2× in this paper · explained in 2 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Triplet Analyzing Unit — 2× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Pre-trained Transformer — 1× in this paper
• Grounding — 1× in this paper · explained in 3 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Standardized Benchmarks — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis
• GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework
• Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
• Correctness is not Faithfulness in RAG Attributions
• Do Large Language Models Understand Logic or Just Mimick Context?

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