LLM Book 5 - RoBERTa, DistilBERT, ELECTRA, ALBERT, and XLNet/ Variants and Improvements of BERT for Absolute Beginners
Table of Contents
1. Introduction
1.1 What is BERT and why is it important?
1.2 How does BERT work?
1.3 What are the main challenges and limitations of BERT?
1.4 What are the objectives and scope of this book?
2. RoBERTa: Robustly Optimized BERT Pretraining Approach
2.1 What is RoBERTa and how does it differ from BERT?
2.2 How does RoBERTa improve the pretraining process of BERT?
2.3 What are the main benefits and drawbacks of RoBERTa?
2.4 How to use RoBERTa for downstream tasks?
3. DistilBERT: Distilled Version of BERT
3.1 What is DistilBERT and how does it differ from BERT?
3.2 How does DistilBERT reduce the size and complexity of BERT?
3.3 What are the main benefits and drawbacks of DistilBERT?
3.4 How to use DistilBERT for downstream tasks?
4. ELECTRA: Efficiently Learning an Encoder that Classifies Token Replacements Accurately
4.1 What is ELECTRA and how does it differ from BERT?
4.2 How does ELECTRA improve the pretraining objective of BERT?
4.3 What are the main benefits and drawbacks of ELECTRA?
4.4 How to use ELECTRA for downstream tasks?
5. ALBERT: A Lite BERT
5.1 What is ALBERT and how does it differ from BERT?
5.2 How does ALBERT reduce the parameters and memory consumption of BERT?
5.3 What are the main benefits and drawbacks of ALBERT?
5.4 How to use ALBERT for downstream tasks?
6. XLNet: Generalized Autoregressive Pretraining for Language Understanding
6.1 What is XLNet and how does it differ from BERT?
6.2 How does XLNet overcome the pretraining limitations of BERT?
6.3 What are the main benefits and drawbacks of XLNet?
6.4 How to use XLNet for downstream tasks?
7. Conclusion
7.1 What are the main similarities and differences among the variants of BERT?
7.2 What are the main challenges and opportunities for future research and development of BERT and its variants? 7.3 What are the main takeaways and recommendations for the readers of this book?
