Can LLMs plan paths in the real world?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916502354001920 |
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| author | Chen, Wanyi Su, Meng-Wen Mehjabin, Nafisa Cummings, Mary L. |
| author_facet | Chen, Wanyi Su, Meng-Wen Mehjabin, Nafisa Cummings, Mary L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17912 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can LLMs plan paths in the real world? Chen, Wanyi Su, Meng-Wen Mehjabin, Nafisa Cummings, Mary L. Artificial Intelligence Robotics 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. |
| title | Can LLMs plan paths in the real world? |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2411.17912 |