Encoder/decoder Text Ai
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%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.)
![Encoder Decoder and Memory pipeline. First, we encode [32] the visual ...](https://www.researchgate.net/profile/Rodrigo_Simas3/publication/361921761/figure/fig6/AS:11431281126148787@1678566796935/Encoder-Decoder-and-Memory-pipeline-First-we-encode-32-the-visual-patterns-of-the_Q640.jpg)
%2C%20encodes%20the%20input%20once%20and%20decodes%20the%20output%20in%20parallel%2C%20boosting%20both%20training%20and%20inference%20efficiency%20by%20avoiding%20duplicate%20input%20encoding%20and%20increasing%20the%20operational%20intensity%20(ratio%20of%20numbers%20of%20arithmetic%20operation%20to%20memory%20access)%20of%20decoding%20process%20by%20sharing%20the%20input%20key-value%20cache.%20We%20achieve%20computation%20reduction%20that%20roughly%20scales%20with%20the%20number%20of%20subtasks%2C%20gaining%20up%20to%204.6x%20speed-up%20over%20state-of-the-art%20models%20for%20dialogue%20state%20tracking%2C%20summarization%2C%20and%20question-answering%20tasks%2C%20with%20comparable%20or%20better%20performance.)
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