Introduction

Large Language Models (LLMs) have emerged as transformative tools capable of understanding and generating human language with unprecedented accuracy. These models, underpinned by the sophisticated transformer architecture, represent a significant leap forward in natural language processing. This article delves into the technical details that make LLMs so powerful, exploring the foundational components such as self-attention mechanisms, token embeddings, and the interplay of linear and softmax layers. By unraveling the complexities of these elements, we aim to provide a comprehensive understanding of how LLMs operate and achieve their remarkable performance in a variety of language-related tasks.

The Transformer Architecture : The Building Block

The transformer architecture is the fundamental building block of all Language Models. The transformer architecture was introduced in the paper “Attention is all you need,” published in December 2017. The transformer abandons CNN and RNN used in previous deep learning tasks.This model is widely used in the field of NLP, such as machine translation, question answering system, text Digest and speech recognition and more.The simplified version of the Transformer Architecture looks like this:

source : Attention is all you need research paper

 

We will understand the components in detail in the following section.

1. Input and Inputs Embeddings: In transformer models, input embeddings convert raw text into dense vectors that capture semantic and syntactic information. These embeddings enable the model to process and understand the text efficiently, capturing complex relationships and dependencies. This transformation is crucial for the model to perform various NLP tasks accurately and effectively.

2. Positional encoding:For earlier neural network models, a method for interpreting the order of words in an input sequence is missing from the transformer model. In order to deal with this problem, the transformer adds an additional vector called Positional embeddings to the input of the encoder layer and decoder layer. Position embeddings can determine the position of the current word, or the distance between different words in a sentence. There are many specific calculation methods for this position vector. In order for the model to capture the order information of the words, we add position-encoding vector information (POSITIONAL ENCODING). The position-encoding vector does not need training, and it has a rule generation method.

3. Encoder:The encoder component in transformer models is responsible for processing input sequences and creating rich, context-aware representations. It consists of multiple layers, each with two main sub-layers: a self-attention mechanism and a feed-forward neural network. Self-attention can help the current node not only focus on the current word, so that it can obtain the semantics of the context and this mechanism allows the encoder to weigh the importance of different tokens in the sequence relative to each other, capturing dependencies regardless of their distance. The feed-forward network then transforms these weighted tokens to create more abstract representations. By stacking several of these layers, the encoder builds a comprehensive understanding of the input sequence, which is essential for tasks like translation, summarization, and more.

4. Outputs (shifted right):In transformer models, “output shifted right” refers to the training technique where the target sequence is shifted by one position. This means each token in the sequence is aligned with the previous token in the model’s output, helping the model learn to predict each token based on the preceding ones. This shifting allows the model to generate sequences in an autoregressive manner, ensuring that each prediction is informed by the context of previously generated tokens, thus improving the coherence and accuracy of the generated sequences.

5. Output Embeddings:In transformers, output embeddings represent the final layer’s processed information before making predictions. They capture contextual meanings of tokens by reflecting their roles within a sequence, adjusting dynamically based on surrounding words. These embeddings are crucial for generating predictions, such as the next token in a sequence or classification labels, and are fine-tuned during training to improve the model’s performance on specific tasks. Essentially, they bridge the model’s internal representations with its output predictions.

6. Decoder:The decoder component in transformer models generates outputs based on the encoded input sequence and previously generated tokens. It comprises multiple layers, each containing three sub-layers: self-attention, encoder-decoder attention, and a feed-forward neural network. The self-attention sub-layer allows the decoder to focus on different positions within the generated sequence, capturing dependencies and context. The encoder-decoder attention sub-layer enables the decoder to attend to the encoder’s output, incorporating relevant information from the input sequence. The feed-forward neural network sub-layer applies non-linear transformations to the attention outputs, helping the model learn complex patterns. Stacking these layers allows the decoder to produce coherent, contextually appropriate sequences for tasks such as translation, text generation, and summarization.

7. Linear layer and Softmax:In transformers, the linear and softmax components play key roles in generating predictions:

i) Linear Layer: After processing input data through the transformer’s layers, the model uses a linear layer to project the high-dimensional output embeddings into the vocabulary size. This linear transformation helps in mapping the hidden representations to the actual token predictions.

ii) Softmax Layer: Following the linear layer, the softmax function is applied to the output. This function converts the linear layer’s raw scores (logits) into probabilities by normalizing them so that their sum equals one. Each value represents the probability of each token in the vocabulary being the next token in the sequence.

Fig. The internal simplified structure of each encoder and decoder.

Self attention concept:

Attention is a technology that allows the model to focus on important information and fully learn and absorb it. It is not a complete model. It should be a technology that can be used in any sequence model.Attention, as the name implies, in the decoding stage, the model will select the context that is most suitable for the current node as input.

Let ’s understand self-attention now. The idea is similar to attention, but self-attention is a train of thought used by Transformer to convert the “understanding” of other related words into words that we normally understand. Let ’s look at an example

“The bull is running high after the election results”

Here whether ‘bull’ represents the animal or stock market, it can be easily judged for humans, but for the machine, it is difficult to judge, self-attention can let the machine associate it with the stock market.
Thus the mechanism captures dependencies and context from the entire sequence, enhancing the model’s ability to understand and generate text.

Conclusion

In summary, the technical intricacies of Large Language Models (LLMs) highlight their remarkable capabilities and complexity. At their core, LLMs leverage the transformer architecture, which facilitates efficient processing and understanding of vast amounts of text by utilizing mechanisms such as self-attention, encoders , decoders, linear projections, and softmax normalization. This architecture allows LLMs to capture deep contextual relationships and generate coherent, contextually relevant outputs. By delving into these technical components—such as token embeddings, attention mechanisms, and output projections—we gain insight into how LLMs achieve their impressive performance across a range of language tasks. Understanding these details not only sheds light on the underlying workings of these powerful models but also informs ongoing advancements in AI and natural language processing.