Summary of Graphtool-instruction: Revolutionizing Graph Reasoning in Llms Through Decomposed Subtask Instruction, by Rongzheng Wang et al.
GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs through Decomposed Subtask Instruction
by Rongzheng Wang, Shuang Liang, Qizhi Chen, Jiasheng Zhang, Ke Qin
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to graph reasoning tasks using large language models (LLMs). Traditional methods fine-tune LLMs with task-specific instructions, but these Text-Instruction methods struggle with performance. The researchers draw inspiration from tool learning and develop Tool-Instruction methods that call specific tools to solve graph problems. However, this approach neglects graph structure information, leading to poor performance on smaller-scale LLMs (less than 13B). To address this issue, the authors introduce GraphTool-Instruction, an innovative approach that decomposes graph reasoning tasks into three subtasks and designs specialized instructions for each. This method can be used as a plug-and-play prompt for different LLMs without fine-tuning. The researchers also develop GTools, a dataset of twenty graph reasoning tasks, and create a graph reasoning LLM called GraphForge based on Llama3-8B. Extensive experiments demonstrate that GraphTool-Instruction achieves state-of-the-art (SOTA) performance compared to Text-Instruction and Tool-Instruction methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can solve complex problems with graphs. Currently, computers use special instructions to solve graph-based tasks, but this approach doesn’t work well for smaller computers. The researchers came up with a new idea that breaks down the task into three simpler steps and gives specific instructions for each step. This makes it easier for computers to learn how to solve graph-based problems without needing to be re-trained. They also created a set of tasks and a special computer program called GraphForge that can help with this type of problem-solving. |
Keywords
» Artificial intelligence » Fine tuning » Prompt
