DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration

arXiv:2311.04799 · cs.CL, cs.AI · Submitted 2023-11-08 · Read on arXiv

cs.CL, cs.AI

Submitted: 2023-11-08

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 8× in this paper · explained in 1 episode
• Embeddings — 7× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• Adaptive Input Representations for Neural Language Modeling
• Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• A Multiscale Visualization of Attention in the Transformer Model
• Cramming: Training a Language Model on a Single GPU in One Day
• GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

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