[Submitted on 24 Sep 2026]
Title:Do LLMs Understand Context? A Knowledge Graph-Based Evaluation Framework
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Abstract:While large language models (LLMs) have achieved remarkable linguistic capabilities, a profound question lingers at their core: do these models truly comprehend context or simply excel at pattern matching on an unprecedented scale? Contextual understanding in LLMs refers to the ability to correctly extract relevant information from a given context, integrate it into a coherent internal representation, and reason over it to produce factually consistent and contextually grounded responses. However, traditional methods such as BiLingual Evaluation Understudy (BLEU) and perplexity simply measure surface-level performance. This reveals a critical gap in question answering (QA), where responses must be contextually grounded rather than simply being memorized associations. To fill this void, we propose a novel knowledge graph (KG) based evaluation framework for LLM contextual understanding in QA. Central to this is Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure combining structural and semantic signals into a single score. In addition, a diagnostic analysis framework is developed to identify and categorize reasoning errors at the triplet level, enabling fine-grained analysis of model failures. Together, across nine benchmarks, S3KG achieves F1 gains of up to $+7.6$ points over the strongest baseline and AUROC up to $0.973$.
Comments: Accepted in : AACL-IJCNLP 2026
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2609.30484 [cs.AI]
(or arXiv:2609.30484v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.30484
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pragatheeswaran Vipulanandan [view email] [v1] Thu, 24 Sep 2026 19:20:08 UTC (1,296 KB)
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