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BERT's success came at a cost: it was enormous. BERT-Large packed 340 million parameters into a model that required significant GPU memory just to fine-tune. Scaling language models further seemed to require more parameters and a proportional increase in memory and compute. But does it have to be this way?
ALBERT (A Lite BERT) challenged this assumption. Published by Google Research in 2019, ALBERT introduced two parameter-reduction techniques that slashed model size by up to 18x while maintaining competitive performance. The key insight was that much of BERT's parameter budget was redundant. By factorizing the embedding matrix and sharing parameters across layers, ALBERT demonstrated that smaller models could learn equally rich representations.
This chapter explores ALBERT's architecture, its parameter-sharing strategies, and how it achieves efficiency without sacrificing quality. We'll implement the core techniques from scratch and examine the trade-offs involved in building lighter transformer models.