Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?

• URL: http://arxiv.org/abs/2405.14379v1
• Date: Thu, 23 May 2024 09:54:54 GMT
• Title: Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?
• Authors: Thomas Greatrix, Roger Whitaker, Liam Turner, Walter Colombo,
• Abstract summary: We observe that Large Language Models (LLMs) are able to perform sophisticated reasoning on problems with a spatial dimension. This points to a significant level of understanding that state-of-the-art LLMs can now achieve.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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