Understanding Large Language Models: The History of LLMs - Part 1

Welcome to my blog series on Large Language Models (LLMs). In this part, we'll dive into the history of LLMs by exploring two seminal advancements: the Encoder-Decoder architecture and the Attention mechanism. These breakthroughs have significantly shaped the development of modern LLMs.

Encoder-Decoder Architecture (2014)

The Encoder-Decoder architecture was introduced in a 2014 paper by Cho et al. and Sutskever et al.. This architecture laid the foundation for many subsequent models in the field of NLP.

What is the Encoder-Decoder Architecture?

The Encoder-Decoder model consists of two main components:

1. Encoder The encoder processes the input sequence and compresses it into a fixed-length context vector, which encapsulates the information of the entire input sequence. This context vector serves as a summary of the input data. The encoder typically uses an LSTM model that takes the input word by word and produces a final output, which is then sent to the decoder.
2. Decoder The decoder takes the context vector generated by the encoder and produces the output sequence. The decoder also uses an LSTM model, which generates the output word by word.

Why is it Important?

The Encoder-Decoder architecture was a major breakthrough because it allowed for more flexible handling of sequences with different lengths. This made it particularly suitable for tasks like machine translation, where the length of sentences in different languages can vary significantly. However, it had its limitations:

• Longer Sentences: The architecture performed well with small sentences. For longer sentences, it struggled to capture the semantic meaning, resulting in unreliable output.
• Initial Inputs: As the input got longer, the model failed to weigh the initial inputs properly, causing the semantic meaning of the translation to change completely. This was the biggest flaw of the Encoder-Decoder model.

“Sadly mistaken, he realized that the job offer was actually an incredible opportunity that would lead to significant personal and professional growth”

Attention Mechanism (2015)

In 2015, Bahdanau et al. introduced the Attention mechanism, which addressed some of the limitations of the basic Encoder-Decoder architecture.

What is the Attention Mechanism?

The Attention mechanism allows the model to focus on different parts of the input sequence at each step of the output generation. Instead of relying on a single context vector to represent the entire input sequence, the model creates a weighted sum of all the input vectors, with the weights dynamically determined at each decoding step.

Why is it Important?

The Attention mechanism significantly improved the performance of Seq2Seq models by allowing them to:

• Capture More Context: Unlike the encoder-decoder used for translation, which has a single context vector, the Attention mechanism has a context vector for every single word. This allows the model to stop at any given point and retrieve the necessary information. At any step of the decoder, it has access to the hidden state of every step of the encoder.
• Select Relevant Information: The Attention mechanism dynamically searches and selects the best-suited word from the encoder. For printing any word, it considers all hidden states and determines the similarity between the output word and each word of the encoder. The word with the highest similarity is then used for prediction.
• Handle Longer Sequences: By focusing on different parts of the input, the model can better handle longer sequences without being overwhelmed by the need to compress all information into a single context vector.

In attention mechanism, all parts of the sentence would be covered “Sadly mistaken, he realized that the job offer was actually an incredible opportunity that would lead to significant personal and professional growth”

Conclusion

In this part of our series, we've explored two foundational advancements in the history of LLMs: the Encoder-Decoder architecture and the Attention mechanism. These innovations have paved the way for more sophisticated models that we use today. In the next part, we'll continue our journey through the history of LLMs by discussing the "Attention is All You Need" paper and the introduction of the Transformer model.

Stay tuned for more insights into the fascinating world of Large Language Models!

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