ALBERT is a language model that addresses scalability issues faced by large language models. It achieves significant reduction in parameters through factorized parameter embedding and cross-layer parameter sharing. ALBERT also replaces the next sentence prediction objective with sentence order prediction. Compared to BERT, ALBERT achieves comparable or better performance on downstream tasks while being faster. However, ALBERT requires more computations due to its longer structures. ALBERT is suited for problems where speed can be traded off for higher accuracy.
Large Language Models, ALBERT — A Lite BERT for Self-supervised Learning
Introduction
In recent years, large language models like BERT have become popular for solving NLP tasks with high accuracy. However, these models have scalability issues, making it challenging to train, store, and use them effectively. To address this, ALBERT was developed in 2020 with the goal of reducing the number of parameters in BERT.
ALBERT
ALBERT is similar to BERT in many ways but has three key differences in its architecture:
1. Factorized Parameter Embedding: ALBERT uses matrix factorization to reduce the number of parameters needed to store embeddings. This makes the model more memory-efficient and reduces the resources required for training.
2. Cross-layer Parameter Sharing: ALBERT shares weights across similar blocks of the model, reducing the memory needed to store parameters. This improves computational efficiency during forward propagation and backpropagation.
3. Sentence Order Prediction: Instead of using next sentence prediction (NSP) like BERT, ALBERT uses sentence order prediction (SOP). This helps the model perform better on downstream tasks and improves its adaptability.
BERT vs ALBERT
ALBERT outperforms BERT on downstream tasks while having fewer parameters. For example, ALBERT xxlarge achieves better performance than BERT large while having only 70% of the parameters. ALBERT large is also faster than BERT large due to parameter size compression.
Conclusion
ALBERT is a promising alternative to BERT for solving NLP tasks. While it requires more computations, it offers higher accuracy. ALBERT is best suited for situations where speed can be traded off for accuracy. As the field of NLP continues to progress, there may be further improvements in the speed of ALBERT models. To explore how AI can transform your company, consider using ALBERT and other AI solutions to automate customer engagement and improve sales processes.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Large Language Models, ALBERT — A Lite BERT for Self-supervised Learning
- Towards Data Science – Medium
- Twitter – @itinaicom
