测试大模型在真实道路中的路径规划能力，发现表现不可靠。
Can LLMs plan paths in the real world?
- 用三个大模型在六种真实场景中测试路径规划
- 所有模型在各场景均出现大量错误，可靠性差
- 适合关注大模型实际应用局限的研究者
随着大语言模型（LLMs）越来越多地融入车辆导航系统，理解其路径规划能力至关重要。我们在多种环境和不同难度下，通过六个真实世界的路径规划场景测试了三个LLMs。实验表明，所有模型在所有场景中均出现大量错误，揭示了它们作为路径规划器的不可靠性。我们建议未来研究应聚焦于实现现实性验证机制、提升模型透明度，并开发更小的模型。
原文摘要 · Abstract (English)
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.
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