Understanding language isn't just about knowing what each word means individually. It's about how words relate to each other, how their order changes meaning, and how context clarifies ambiguity. Early AI models often struggled with this, processing text in a rigid, sequential manner.
The Limitations of Sequential Processing
Before Transformers, models like Recurrent Neural Networks (RNNs) processed text word-by-word, passing a 'hidden state' from one step to the next. This was like trying to remember a long conversation by only recalling the last few sentences, quickly losing track of earlier important details.
RNN's Short-Term Memory
Word 1 (e.g., 'The')
Initial input.
An RNN processes the first word and updates its internal state.
Traditional RNNs struggle to connect words that are far apart in a sentence (e.g., 'The **cat**, which had chased a mouse through the garden and up a tree, **was** hungry'). The subject 'cat' and verb 'was' are linked, but the distance makes it hard for a sequential model to retain this relationship.