I’m starting a sentiment analysis project and everyone keeps telling me to use BERT or RoBERTa instead of building an RNN from scratch. Why has Transfer Learning become so dominant in Deep Learning for NLP? Is there ever a situation where a custom-built LSTM would actually be more beneficial than a fine-tuned Transformer?
3 answers
Transformers use "Attention" mechanisms which allow them to process words in parallel, making them much faster to train than sequential RNNs.
Transfer Learning is the standard because it solves the data scarcity problem. Training a model to "understand" the nuances of English takes billions of words, which most companies don't have. Models like BERT are pre-trained on massive datasets (Wikipedia, BookCorpus) and already understand context and syntax. When you "fine-tune" it, you are just teaching it your specific labels. An LSTM built from scratch would require significantly more labeled data to even come close to the same accuracy. The only time I'd use a custom LSTM is for very niche, low-resource languages or sequence tasks with extremely limited memory constraints.
Are you concerned about the computational cost of running a Transformer model in production compared to a lightweight LSTM?
That is a valid concern, Richard. Transformers are much "heavier." However, with techniques like DistilBERT or TinyBERT, you can get 95% of the performance with a fraction of the size. I’ve found that the time saved on development and the massive boost in accuracy usually outweighs the extra millisecond of inference latency. For most business use cases, the accuracy of a fine-tuned Transformer is simply too good to pass up for a "faster" but dumber LSTM.
Thomas hits the nail on the head. The parallelization of Transformers is what allowed us to scale Deep Learning to the sizes we see today with LLMs.