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Status: Unchecked ×Keyword: transformer models ×Clear all

6 claims from 4 papers

1. Computer Science › Natural Language Processing Techniques

   Attention Is All You Need

   Vaswani, Shazeer, Parmar et al. · 2025

   The paper proposes the Transformer, a network built only on attention mechanisms, and reports better translation quality and shorter training times than leading recurrent or convolutional models.

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   1. UncheckedThe Transformer model scored 28.4 BLEU on WMT 2014 English-to-German translation, over 2 BLEU above earlier best results, including ensembles.“Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU.”
   2. UncheckedOn WMT 2014 English-to-French translation, the Transformer reports a single-model BLEU of 41.8 after 3.5 days on eight GPUs, at a fraction of rivals' training cost.“On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.”
   3. UncheckedThe paper reports that the Transformer, a model built only on attention, also worked well on English constituency parsing, with large and with limited training data.“We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.”

2. Computer Science › Advanced Neural Network Applications

   The State of Sparsity in Deep Neural Networks

   Trevor, Elsen and Hooker · arXiv (Cornell University) · 2019

   The authors tested three sparsity techniques on a Transformer for translation and ResNet-50 for ImageNet, finding simple magnitude pruning competitive, and called for large-scale benchmarks in model compression.

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   1. UncheckedOn two large tasks, complex sparsity methods that worked well on smaller datasets performed inconsistently, while simple magnitude pruning did as well or better.“Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results.”

3. Computer Science › Topic Modeling

   A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

   Fields, Chovanec and Madiraju · IEEE Access · 2024

   A survey of transformer-based text classification covering history, text-only and multimodal inputs, text length, accuracy, cost, safety, ethics, bias and copyright.

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   1. UncheckedA review of accuracy across 358 datasets and 20 applications reports that large language models are not always the most accurate or cheapest option.“Furthermore, the accuracy on 358 datasets across 20 applications is reviewed and unexpected results emerge which show that LLMs are not always the most accurate or least expensive option.”

4. Computer Science › Topic Modeling

   RWKV: Reinventing RNNs for the Transformer Era

   Peng, Alcaide, Anthony et al. · arXiv (Cornell University) · 2023

   The paper proposes RWKV, an architecture combining Transformer-style parallel training with RNN-style efficient inference, scaled to 14 billion parameters and reported to match similarly sized Transformers.

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   1. UncheckedRWKV uses linear attention so it can run as a Transformer when training and as an RNN when generating, with constant cost per step at inference.“Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference.”