GraphArena: Benchmarking Large Language Models on Graph Computational Problems

• URL: http://arxiv.org/abs/2407.00379v1
• Date: Sat, 29 Jun 2024 09:19:23 GMT
• Title: GraphArena: Benchmarking Large Language Models on Graph Computational Problems
• Authors: Jianheng Tang, Qifan Zhang, Yuhan Li, Jia Li,
• Abstract summary: "arms race" of Large Language Models (LLMs) demands novel, challenging, and diverse benchmarks to examine their progresses. We introduce GraphArena, a benchmarking tool to evaluate models on graph computational problems using million-scale real-world graphs.
• Score: 25.72820021030033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The "arms race" of Large Language Models (LLMs) demands novel, challenging, and diverse benchmarks to faithfully examine their progresses. We introduce GraphArena, a benchmarking tool designed to evaluate LLMs on graph computational problems using million-scale real-world graphs from diverse scenarios such as knowledge graphs, social networks, and molecular structures. GraphArena offers a suite of 10 computational tasks, encompassing four polynomial-time (e.g., Shortest Distance) and six NP-complete challenges (e.g., Travelling Salesman Problem). It features a rigorous evaluation framework that classifies LLM outputs as correct, suboptimal (feasible but not optimal), or hallucinatory (properly formatted but infeasible). Evaluation of 10 leading LLMs, including GPT-4o and LLaMA3-70B-Instruct, reveals that even top-performing models struggle with larger, more complex graph problems and exhibit hallucination issues. Despite the application of strategies such as chain-of-thought prompting, these issues remain unresolved. GraphArena contributes a valuable supplement to the existing LLM benchmarks and is open-sourced at https://github.com/squareRoot3/GraphArena.

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