Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks

• URL: http://arxiv.org/abs/2509.19347v1
• Date: Wed, 17 Sep 2025 08:15:14 GMT
• Title: Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks
• Abstract summary: Large Language Models (LLMs) are increasingly used for tasks involving Knowledge Graphs (KGs)<n>We propose a complementary task characterization approach using three complexity frameworks from cognitive psychology.<n>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.
• Score: 0.13108652488669734
• License: http://creativecommons.org/licenses/by/4.0/
• 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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