Summary
The advent of the transformer significantly propelled the field of NLP forward, serving as the foundation for today’s cutting-edge generative language models. This chapter delineated the progression of NLP that paved the way for this pivotal innovation. Initial statistical techniques such as count vectors and TF-IDF were adept at extracting rudimentary word patterns, yet they fell short in grasping semantic nuances.
Incorporating neural language models marked a stride toward more profound representations through word embeddings. Nevertheless, recurrent networks encountered hurdles in handling longer sequences. This inspired the emergence of CNNs, which introduced computational efficacy via parallelism, albeit at the expense of global contextual awareness.
The inception of attention mechanisms emerged as a cornerstone. In 2017, Vaswani et al. augmented these advancements, unveiling the transformer architecture. The hallmark self-attention mechanism of the transformer...
