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%2C there has been a shift towards linearization-based methods%2C which process structured data as sequential token streams%2C diverging from approaches that explicitly model structure%2C often as a graph. Crucially%2C there remains a gap in our understanding of how these linearization-based methods handle structured data%2C which is inherently non-linear. This work investigates the linear handling of structured data in encoder-decoder language models%2C specifically T5. Our findings reveal the model's ability to mimic human-designed processes such as schema linking and syntax prediction%2C indicating a deep%2C meaningful learning of structure beyond simple token sequencing. We also uncover insights into the model's internal mechanisms%2C including the ego-centric nature of structure node encodings and the potential for model compression due to modality fusion redundancy. Overall%2C this work sheds light on the inner workings of linearization-based methods and could potentially provide guidance for future research.?quality=80&w=800)
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