Can LLMs plan paths in the real world?
This highlights a critical safety issue for vehicle navigation systems relying on LLMs, indicating incremental research by exposing limitations.
The study tested three large language models (LLMs) in real-world path-planning scenarios and found they made numerous errors, demonstrating unreliability in this task.
As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planning scenarios in various settings and with various difficulties. Our experiments showed that all LLMs made numerous errors in all scenarios, revealing that they are unreliable path planners. We suggest that future work focus on implementing mechanisms for reality checks, enhancing model transparency, and developing smaller models.