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%252C+there+has+been+a+shift+towards+linearization-based+methods%252C+which+process+structured+data+as+sequential+token+streams%252C+diverging+from+approaches+that+explicitly+model+structure%252C+often+as+a+graph.+Crucially%252C+there+remains+a+gap+in+our+understanding+of+how+these+linearization-based+methods+handle+structured+data%252C+which+is+inherently+non-linear.+This+work+investigates+the+linear+handling+of+structured+data+in+encoder-decoder+language+models%252C+specifically+T5.+Our+findings+reveal+the+model%27s+ability+to+mimic+human-designed+processes+such+as+schema+linking+and+syntax+prediction%252C+indicating+a+deep%252C+meaningful+learning+of+structure+beyond+simple+token+sequencing.+We+also+uncover+insights+into+the+model%27s+internal+mechanisms%252C+including+the+ego-centric+nature+of+structure+node+encodings+and+the+potential+for+model+compression+due+to+modality+fusion+redundancy.+Overall%252C+this+work+sheds+light+on+the+inner+workings+of+linearization-based+methods+and+could+potentially+provide+guidance+for+future+research.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.613f26f2fe374bd5c5b17c3fbed84211%3frik%3dz7hy6ISH2B0K7g%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Encoding+Language&FORM=IRPRST&ck=7B479258DD04ED587F94554217699A1B&selectedIndex=117&itb=0)
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