The potential for Large Language Models (LLMs) to generate new information offers a potential step change for research and innovation. This is challenging to assert as it can be difficult to determine what an LLM has previously seen during training, making "newness" difficult to substantiate. In this paper we observe that LLMs are able to perform sophisticated reasoning on problems with a spatial dimension, that they are unlikely to have previously directly encountered. While not perfect, this points to a significant level of understanding that state-of-the-art LLMs can now achieve, supporting the proposition that LLMs are able to yield significant emergent properties. In particular, Claude 3 is found to perform well in this regard.

翻译：大型语言模型（LLMs）生成新信息的潜力为研究与创新带来了潜在的变革性机遇。这一主张难以确证，因为通常难以确定LLM在训练过程中曾接触过哪些信息，从而使“新颖性”难以得到实质性验证。本文研究发现，LLM能够对具有空间维度且此前不太可能直接接触过的问题进行复杂推理。尽管表现并非完美，但这表明当前最先进的LLM已能达到相当程度的理解水平，从而支持了LLM能够产生显著涌现特性的命题。特别地，Claude 3模型在这方面表现出色。