Summary of Grapharena: Evaluating and Exploring Large Language Models on Graph Computation, by Jianheng Tang et al.
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
by Jianheng Tang, Qifan Zhang, Yuhan Li, Nuo Chen, Jia Li
First submitted to arxiv on: 29 Jun 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 A new benchmarking tool called GraphArena has been introduced to evaluate Large Language Models (LLMs) on real-world graph computational problems. This tool offers a suite of tasks, including polynomial-time and NP-complete challenges, to test the abilities of LLMs in solving graph problems. The evaluation framework is rigorous and classifies LLM outputs into four categories: correct, suboptimal, hallucinatory, or missing. An analysis of over 10 LLMs reveals that even top-performing models struggle with larger, more complex graph problems and exhibit hallucination issues. To address this issue, four potential solutions are explored, including chain-of-thought prompting, instruction tuning, code writing, and scaling test-time compute. Each solution demonstrates unique strengths and limitations. GraphArena complements existing LLM benchmarks and is open-sourced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Arena is a new way to test how good language models are at solving graph problems. It has four levels of tasks that get harder as you go, from simple to very hard. The tool looks at what the model says is the answer and gives it one of four scores: correct, close but not quite right, completely made up, or missing altogether. When they tested 10 top models, they found that even the best ones struggle with really tough graph problems and sometimes make things up that aren’t true. To fix this problem, scientists came up with four ways to help models do better on graph problems: making them think step by step, giving them more information about what to do, having them write their own code, or making it faster for them to test answers. |
Keywords
» Artificial intelligence » Hallucination » Instruction tuning » Prompting
