Natural language processing, or NLP, has been a significant field of study for decades, with researchers and enthusiasts eagerly waiting on how this technology would transform the way we communicate with machines.
Before 2017, the widely used approach for text-to-text generation tasks was the recurrent neural network (RNN) using an Encoder-Decoder architecture. In this pattern, the encoder would process the input text word by word, and its final hidden state would be passed to the decoder as a compressed “context vector.” This context vector was a bottleneck; it had to encode the entire meaning of the input sentence, no matter how long, into a single vector. This was the reason why the model would often “forget” the earlier parts of the text, leading to incorrect information.
This all changed when Google published the paper “Attention Is All You Need” in 2017. This paper didn’t just introduce an “attention mechanism” as an add-on; it introduced a revolutionary neural network architecture called the Transformer, (Yeah that was the start of the LLMs we use today) which was built entirely on this new concept.
The core concept of the Transformer was the self-attention mechanism. This mechanism changed how the model processes text. Instead of relying on a single context vector, the self-attention mechanism allowed the model to weigh the importance of different words in the input sequence when generating each word of the output. Essentially, for every word it generates, the model can “look back” at the entire input sequence and decide which words are most relevant to the task at hand. This ability to dynamically focus on different parts of the input sequence was a massive leap forward. It solved the bottleneck problem of RNNs and enabled models to handle longer and more complex sequences with accuracy.
Why the need for this you ask? Well for one, Same words can have different meaning based on the sentence in which they are used. This creates need for look up (as same words can same vector representation, this problem was solved by attention mechanism) for the transformers to accurately predict the next word in the sequence. For eg, consider river bank and bank robbery, the meaning of bank in those 2 statements is different.
LLMs trying to predict the next token without self-attention mechanism
The Transformer architecture, powered by the self-attention mechanism, laid the foundation for the Large Language Models (LLMs) and Generative AI that we have today, such as GPT-5, Claude 4.1, and Gemini 2.5 Pro. The ability of these models to understand context, generate coherent text, and even perform complex reasoning tasks is a direct result of the leap in efficiency and capability that the attention mechanism provided. It truly was the catalyst that made the current AI revolution possible.
With the invention of the Transformer architecture started the race to create the greatest, fastest and baddest model of the world. What according to you is the best model available in the tech space right now. The last I checked Grok was the best model (according to Elon) and He was crying about why it wasn’t listed in the App Store’s top AI app.
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