Summary of Gracore: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models, by Zike Yuan et al.
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
by Zike Yuan, Ming Liu, Hui Wang, Bing Qin
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents GraCoRe, a benchmark for evaluating Large Language Models’ (LLMs) graph comprehension and reasoning abilities. Unlike existing benchmarks that focus on pure graph understanding, GraCoRe assesses models across various graph types and defines detailed capability metrics. The benchmark uses a three-tier hierarchical taxonomy to categorize and test LLMs on pure graph and heterogeneous graphs, subdividing capabilities into 10 distinct areas tested through 19 tasks. The authors evaluate four closed-source and eight open-source LLMs using 11 datasets with 5,140 graphs of varying complexity. Key findings reveal that OpenAI o1 model has impressive comprehension and reasoning capabilities, semantic enrichment enhances reasoning performance, node ordering impacts task success, and processing longer texts does not necessarily improve graph comprehension or reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about testing how well Large Language Models understand graphs. Graphs are like maps that show connections between things. The problem is that current tests only look at simple graphs, so we don’t know if these models can handle more complex ones. This paper introduces a new test called GraCoRe, which checks how well the models work on different types of graphs and in various situations. They tested eight different models using many different graphs and found some surprising things, like one model that is really good at understanding graphs, but another model that’s not so great even with extra help. |
