Causal Masking on Spatial Data: An Information-Theoretic Case for Learning Spatial Datasets with Unimodal Language Models
NeutralArtificial Intelligence
A recent study explores the implications of causal masking in language models when applied to spatial data. Traditionally, causal masking is seen as unsuitable for nonsequential data, leading to the use of sequential linearizations. This research is significant as it addresses the information loss that may occur with causal masking in spatial contexts, a topic that has not been thoroughly examined. Understanding this relationship could enhance the effectiveness of language models in processing complex spatial datasets.
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