Summary of Can Llms Plan Paths in the Real World?, by Wanyi Chen et al.
Can LLMs plan paths in the real world?
by Wanyi Chen, Meng-Wen Su, Nafisa Mehjabin, Mary L. Cummings
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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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 novel study evaluates the path-planning capabilities of large language models (LLMs) in vehicle navigation systems. Three LLMs were tested through six real-world scenarios with varying difficulties, revealing that they all made significant errors. The results highlight the limitations of LLMs as reliable path planners and suggest future research directions, including implementing reality checks, enhancing model transparency, and developing smaller models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers tested how well large language models can help plan routes in cars. They used three different models to test them on six real-world scenarios that were easy or hard to navigate. The results showed that all the models made mistakes almost all the time. This means they’re not good at planning routes and might need some changes to be more reliable. |
