Attention Is All You Need — abstract AI rewrite
Real humanize rewrite case study: Attention Is All You Need — abstract AI rewrite. Side-by-side comparison of AI-generated text and EditNow's multi-round rewrite output, with AI detection score delta.
Original AI-generated
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. The Transformer allows for significantly more parallelization than recurrent models, enabling training on much larger datasets in a fraction of the time. By relying entirely on self-attention mechanisms, the model can directly relate different positions of a sequence, capturing long-range dependencies more effectively than traditional RNNs or CNNs. Our architecture consists of stacked encoder and decoder layers, each containing multi-head self-attention mechanisms and position-wise feed-forward networks, along with residual connections and layer normalization to facilitate optimization. Positional encodings are added to the input embeddings to provide the model with information about the relative and absolute positions of tokens in the sequence. Experiments on machine translation and other sequence-to-sequence tasks demonstrate that the Transformer achieves state-of-the-art performance while being substantially more efficient to train.
After EditNow
Most sequence-to-sequence transduction systems rely on recurrent or convolutional neural networks, form as an encoder plus a decoder. Many prominent variants link the encoder and decoder using attention mechanisms. This insert the Transformer architecture, a leaner design that keeps only attention and removes recurrence and convolutions entirely. Because it parallelizes computation far more than recurrent models, it can train on much larger datasets in a small share of the common time. It also links any two positions in a sequence instantly via self-attention. In machine translation and other sequence-to-sequence circumstance, Experiments show that the Transformer achieves state-of-the-art performance while remaining relatively simple to train and quick than former approaches. To retain order information, positional encodings are added to the input embeddings so the model can use both relative and absolute token positions. Experiments on machine translation and related sequence-to-sequence tasks show that this approach reaches state-of-the-art results while remain substantially easy and faster to train.