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| # 🪄 GraphWiz: An Instruction-Following Language Model for Graph Problems |
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| Project Page: [https://graph-wiz.github.io/](https://graph-wiz.github.io/) |
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| Paper: [https://arxiv.org/abs/2402.16029.pdf](https://arxiv.org/abs/2402.16029) |
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| Code: [https://github.com/nuochenpku/Graph-Reasoning-LLM](https://github.com/nuochenpku/Graph-Reasoning-LLM) |
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| ## About Mathoctopus |
| This project aims at leveraging instruction-tuning to build a powerful instruction-following LLM that can map textural descriptions of graphs and structures, and then solve different graph problems explicitly in natural language. |
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| - **GraphWiz**, a series of instruction-following LLMs that have strong graph problem-solving abilities and output explicit reasoning paths. |
| - **GraphInstruct**, which offers over 72.5k training samples across nine graph problem |
| tasks, ranging in complexity from linear and polynomial to NP-complete, extending the scope, scale, |
| and diversity of previous benchmarks. |
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