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arXiv · 2509.19347

Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks

Sara Todorovikj·Lars-Peter Meyer·Michael Martin
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Abstract

Large Language Models (LLMs) are increasingly used for tasks involving Knowledge Graphs (KGs), whose evaluation typically focuses on accuracy and output correctness. We propose a complementary task characterization approach using three complexity frameworks from cognitive psychology. Applying this to the LLM-KG-Bench framework, we highlight value distributions, identify underrepresented demands and motivate richer interpretation and diversity for benchmark evaluation tasks.

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Sara Todorovikj, Lars-Peter Meyer, Michael Martin. 2025-09-17. Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks. https://arxiv.org/abs/2509.19347

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