INTRODUCTION TO LLMS

Some say ignorance is bliss, I beg to differ. You may have wondered about large language models (LLMs), or maybe not, let’s be honest. Let me educate you on how LLMs function, and their applications. Are you curious now? I’ll guide you into the realm of LLMs.

LLMs or extensively known as Large Language Models, are advanced deep learning models specifically designed to process human languages. They belong to a type of machine learning model capable of performing various natural language processing tasks, such as; generating text and language translation. At the heart of these models lies a powerful transformer model where the magic happens.

You are most likely thinking; "What exactly is a Transformer Model?" Transformers are neural networks that excel at understanding and contextual comprehension through sequential data analysis. They rely on a modern mathematical technique known as attention or self-attention, which was introduced in the 2017 paper "Attention is All You Need" by Vaswani et al. Prior to the emergence of transformers, text comprehension in machines relied on models based on recurrent neural networks. These models processed input text one word or character at a time and generated an output once the entire input was consumed. While effective, they sometimes "forgot" what occurred at the beginning of the sequence when reaching the end. In contrast, the attention mechanism in transformers allows the model to comprehend the entire sentence or paragraph simultaneously, rather than one word at a time. This enables the transformer model to grasp the context of words more effectively. Nowadays, state-of-the-art language processing models are predominantly based on transformers.

The transformer model employs an encoder/decoder architecture, with the encoder handling the input sequence and the decoder generating the output sequence. The input text undergoes tokenization using a byte pair encoding tokenizer, with each token converted into a vector via word embedding.

The encoder comprises multiple encoding layers that processes the input iteratively, while the decoder consists of decoding layers that generate the output based on the information from the encoder. Both encoder and decoder layers utilize an attention mechanism, which calculates the relevance of different parts of the input or output sequence.

The attention mechanism operates through scaled dot-product attention, wherein attention weights are simultaneously calculated between each token in the sequence. These weights determine the importance of other tokens in generating the output for a specific token. The transformer model learns three weight matrices: query weights, key weights, and value weights, which are employed to calculate attention weights.

To enhance the model's ability to capture diverse forms of relevance, the transformer model incorporates multiple attention heads in each layer. Each attention head attends to different relationships between tokens, allowing the model to capture various dependencies. This parallel processing of attention heads enables efficient computation.

To prevent information leakage during training, the decoder utilizes a masked attention mechanism. This mechanism eliminates attention links between specific token pairs, ensuring that the decoder doesn't have access to future tokens.

Another crucial aspect of the transformer model is positional encoding. It provides information about the relative positions of tokens within the input sequence. Positional encoding entails representing token positions through fixed-size vectors.

The transformer model employs an encoder/decoder architecture with attention mechanisms to process input sequences and generate output sequences. It leverages scaled dot-product attention and multiple attention heads to capture different forms of relevance. Furthermore, the model incorporates positional encoding to retain information about token positions in the sequence.

In summary, Large Language Models(LLMs) are transformer models scaled up to a significant size. Due to their size, these models are typically not runnable on a single computer and are instead provided as a service through APIs or web interfaces. Large language models are trained on vast amounts of text data, allowing them to learn the patterns and structures of language.

For instance, the GPT-3 model, which powers the ChatGPT service, was trained on a massive corpus of text from the internet, including books, articles, websites, and other sources. Through this training process, the model learns the statistical relationships between words, phrases, and sentences, enabling it to generate coherent and contextually relevant responses when given a prompt or query.

Due to the extensive training data, the GPT-3 model possesses knowledge of various topics and can understand multiple languages. This enables it to generate text in different styles. While it may seem impressive that large language models can perform tasks like translation, text summarization, and question answering, it is not surprising when considering that these models are trained to match specific patterns and structures present in the provided prompts.

That wasn’t so hard now, was it?