Language Models

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<p>125,000</p> Signup and view all the answers

<p>Two parameter-reduction techniques presented in the paper are lowering memory consumption and increasing the training speed of BERT.</p> Signup and view all the answers

<p>The purpose of using a self-supervised loss is to focus on modeling inter-sentence coherence and to consistently help downstream tasks with multi-sentence inputs.</p> Signup and view all the answers

<p>The best model in the paper establishes new state-of-the-art results on benchmarks while having fewer parameters compared to BERT-large.</p> Signup and view all the answers

<p>Factorized embedding parameterization, Cross-layer parameter sharing, Smoother transitions between layers</p> Signup and view all the answers

<p>Cross-layer parameter sharing</p> Signup and view all the answers

<p>$O(V \times E + E \times H)$</p> Signup and view all the answers

<p>The transitions are much smoother for ALBERT than for BERT</p> Signup and view all the answers

<p>The best embedding size under the all-shared condition for the ALBERT base model is 128.</p> Signup and view all the answers

<p>The average F1 score for SQuAD1.1 for the ALBERT base not-shared model with an embedding size of 64 is 88.7.</p> Signup and view all the answers

<p>The total number of parameters for the ALBERT base all-shared model with an embedding size of 768 is 108M.</p> Signup and view all the answers

<p>The purpose of the next-sentence prediction (NSP) loss in BERT is to improve performance on downstream tasks, such as natural language inference, that require reasoning about the relationship between sentence pairs.</p> Signup and view all the answers

<p>Subsequent studies found NSP's impact unreliable and decided to eliminate it because there was an improvement in downstream task performance across several tasks.</p> Signup and view all the answers

<p>ALBERT uses a sentence-order prediction (SOP) loss instead of NSP to model inter-sentence coherence.</p> Signup and view all the answers

<p>The advantages of ALBERT's design choices compared to BERT include smaller parameter size and improved parameter efficiency.</p> Signup and view all the answers

<p>The RACE test is a reading comprehension task designed for middle and high-school English exams in China. According to the text, the machine accuracy on the RACE test has improved from 44.1% to 83.2% to 89.4%.</p> Signup and view all the answers

<p>The main reason for the improvement in machine performance on the RACE test is the current ability to build high-performance pretrained language representations.</p> Signup and view all the answers

<p>According to the text, evidence from improvements in machine performance on the RACE test suggests that having a large network is crucial for achieving state-of-the-art performance.</p> Signup and view all the answers

<p>The two parameter reduction techniques used in the ALBERT architecture are factorized embedding parameterization and cross-layer parameter sharing.</p> Signup and view all the answers

<p>Gradient checkpointing is a method proposed by Chen et al. (2016) to reduce the memory requirement of large models by storing only the necessary intermediate activations. It achieves this by performing an extra forward pass during training to reconstruct each layer’s activations from the next layer, thus reducing the need to store intermediate activations and reducing memory consumption.</p> Signup and view all the answers

<p>ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text, while BERT uses a loss based on predicting whether the second segment in a pair has been swapped with a segment from another document.</p> Signup and view all the answers

<p>Sentence ordering is a more challenging pretraining task compared to predicting whether segments have been swapped. It also has more usefulness for certain downstream tasks.</p> Signup and view all the answers

<p>ALBERT's parameter-reduction techniques focus on reducing memory consumption and increasing training speed, while Raffel et al.'s model parallelization technique aims to train a giant model by dividing it into smaller parts. These techniques have different goals and approaches.</p> Signup and view all the answers

<p>The purpose of ALBERT's parameter-reduction techniques is to lower memory consumption and increase training speed.</p> Signup and view all the answers

<p>The self-supervised loss used in ALBERT focuses on modeling inter-sentence coherence.</p> Signup and view all the answers

<p>Comprehensive empirical evidence shows that ALBERT performs better and scales much better compared to the original BERT.</p> Signup and view all the answers