Can LLMs plan paths in the real world?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 26 Nov 2024.
Builds on
9 of 15 references.
References · 9
- 2023Factuality Challenges in the Era of Large Language Modelscited by 126
- 2024Taming Overconfidence in LLMs: Reward Calibration in RLHFcited by 112
- 2023Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Methodcited by 97
- 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)cited by 85
- 2024Can Large Language Models Reason and Plan?cited by 82
- 2023VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street Viewcited by 67
- 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
- 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 45
- 2024The Larger the Better? Improved LLM Code-Generation via Budget Reallocationcited by 43
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Citing papers per year: 2025: 2.
2 papers.