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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19347 |
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| _version_ | 1866912601769771008 |
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| author | Todorovikj, Sara Meyer, Lars-Peter Martin, Michael |
| author_facet | Todorovikj, Sara Meyer, Lars-Peter Martin, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks Todorovikj, Sara Meyer, Lars-Peter Martin, Michael Computation and Language 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. |
| title | Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.19347 |