text:   Representations",8,"['What happens when you Fourier Transform a BERT neuron?\n\nSignal processing can reveal (+manipulate!) multiscale linguistic structure in BERT neurons!\n\nNew #NeurIPS2020 paper w/ @jurafsky and Noah Goodman \n\nPaper: <LINK> \n@stanfordnlp &amp; @StanfordAILab\n\n👇 1/ <LINK>', 'Linguistic phenomena occur at different scales, including\n– within a word (e.g. morphology)\n– between words (syntax)\n– across utterances (discourse) \n– across paragraphs (topic)\n\nBut to what extent are these scales captured in the representations of pretrained models?\n\n2/ https://t.co/3yj11c1tPK', 'It turns out that many BERT neurons exhibit *multiscale* structure across an input—this neuron changes both rapidly between adjacent tokens and gradually across the input\n\nSpectral analysis lets us disentangle these scales by treating these activations as a digital signal!\n\n3/ https://t.co/b5t2VdkkJ4', ""After doing this, we show through probing experiments that different parts of a neuron's frequency spectrum capture knowledge of NLP tasks at different scales! \n\nLow frequencies correspond to topic, higher frequencies to part of speech, and middle ones to dialog acts.\n\n4/ https://t.co/gOvTR0u304"", 'We can also use spectral filters to *specialize* neurons to different scales of structure during pretraining, using what we call a prism layer.\n\nThis produces single multiscale representations which perform comparably or better on all tasks.\n\n5/ https://t.co/7q1YTx71T1', 'The prism layer also improves modeling of long-range dependencies—the BERT + Prism model outperforms BERT on masked language modeling problems without local context!\n\n6/ https://t.co/5ypM5EhM2e', 'Spectral filters are super easy to incorporate into existing models—it just takes a couple lines of code and an off-the-shelf PyTorch library (https://t.co/NK9ZMyHdiO)\n\n7/ https://t.co/TScZp0SkLt', ""It was great working on this with @jurafsky and Noah Goodman, and we're excited to see what folks do with these methods, both for interpretability and in building better models!\n\nPaper: https://t.co/kTO0WGjUqZ\n\n🌇 8/8""]",20,11,1881