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