![Natural Language Processing (NLP) [A Complete Guide]](https://wordpress.deeplearning.ai/wp-content/uploads/2022/10/11.-Auto-Encoder_Captioned-1024x577.png)
%2C%20there%20has%20been%20a%20shift%20towards%20linearization-based%20methods%2C%20which%20process%20structured%20data%20as%20sequential%20token%20streams%2C%20diverging%20from%20approaches%20that%20explicitly%20model%20structure%2C%20often%20as%20a%20graph.%20Crucially%2C%20there%20remains%20a%20gap%20in%20our%20understanding%20of%20how%20these%20linearization-based%20methods%20handle%20structured%20data%2C%20which%20is%20inherently%20non-linear.%20This%20work%20investigates%20the%20linear%20handling%20of%20structured%20data%20in%20encoder-decoder%20language%20models%2C%20specifically%20T5.%20Our%20findings%20reveal%20the%20model's%20ability%20to%20mimic%20human-designed%20processes%20such%20as%20schema%20linking%20and%20syntax%20prediction%2C%20indicating%20a%20deep%2C%20meaningful%20learning%20of%20structure%20beyond%20simple%20token%20sequencing.%20We%20also%20uncover%20insights%20into%20the%20model's%20internal%20mechanisms%2C%20including%20the%20ego-centric%20nature%20of%20structure%20node%20encodings%20and%20the%20potential%20for%20model%20compression%20due%20to%20modality%20fusion%20redundancy.%20Overall%2C%20this%20work%20sheds%20light%20on%20the%20inner%20workings%20of%20linearization-based%20methods%20and%20could%20potentially%20provide%20guidance%20for%20future%20research.)
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![[2304.04052] Decoder-Only or Encoder-Decoder? Interpreting Language ...](https://ar5iv.labs.arxiv.org/html/2304.04052/assets/x1.png)
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