Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?
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.
Paper Type: Short
Research Area: NLP Applications
Research Area Keywords: mathematical NLP, generalization, reasoning
Contribution Types: Position papers
Languages Studied: english
Submission Number: 55
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