Evaluation of Contextual Understanding in Large Language Models
Large Language Models (LLMs) demonstrate impressive performance across diverse NLP tasks, yet their ability to exhibit genuine contextual understanding remains uncertain. Traditional evaluation metrics such as perplexity, BiLingual Evaluation Understudy (BLEU), or surface-level accuracy fail to reveal how well LLMs extract, integrate, and reason over contextual information--a gap particularly critical in question answering, where models must align responses with contextually grounded knowledge rather than memorized associations. We propose a novel knowledge graph-based evaluation framework introducing Semantic Structural Similarity for KGs (S3KG), a hybrid similarity measure integrating s...
| Authors | Subavarshana Arumugam, Mamta Nallaretnam, Kithuni Wickramasinghe et al. |
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| Primary category | cs.CL |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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