Summary of Look Further Ahead: Testing the Limits Of Gpt-4 in Path Planning, by Mohamed Aghzal et al.
Look Further Ahead: Testing the Limits of GPT-4 in Path Planning
by Mohamed Aghzal, Erion Plaku, Ziyu Yao
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, but still struggle with long-horizon planning. To address this challenge, we propose path planning tasks as a benchmark to evaluate LLMs’ ability to navigate complex trajectories under geometric constraints. Our proposed benchmark systematically tests path-planning skills in challenging settings. We examined GPT-4’s planning abilities using different task representations and prompting approaches, finding that framing prompts as Python code and decomposing long trajectory tasks improve GPT-4’s path planning effectiveness. However, these approaches do not obtain optimal paths and struggle with generalizing over extended horizons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at many things, but they still have trouble making plans that take a long time to happen. To help figure this out, we created some special tasks that test how well LLMs can make plans in complex situations. We used these tasks to study GPT-4’s planning abilities and found that giving it special instructions and breaking down big tasks into smaller ones helped it do better. However, even with these improvements, the model still didn’t come up with perfect plans and struggled to keep going over a long time. |
Keywords
» Artificial intelligence » Gpt » Prompting
