Large Language Models Unveiled: A Practical Approach to Advanced Text Generation

You must be familiar with ChatGPT, which you might have used for one purpose or another. GPT, the model behind the famous ChatGPT, belongs to a category of models called the large language models.

Large language models (LLMs) represent a category of artificial intelligence (AI) systems capable of understanding, generating text, and executing various other functions. These models are developed through training on extensive datasets and consist of neural networks known as transformer models. These transformer models are designed to grasp context and meaning by analyzing the connections within sequential data. Consequently, LLMs are adept at handling a wide range of tasks within the field of natural language processing (NLP).

Large language models are being widely used and have spread across almost all domains, not just limited to information technology, but also including manufacturing, retail, and more. But how do these models understand and process user queries so efficiently? How do they comprehend the content of user questions? Let's try to understand this in this blog.

Additionally, this will be the start of a series of blogs on large language models which will deep dive into various areas of:

• Generative AI technical architecture
• Deployment, usage, and prompt engineering
• Fine-tuning the models
• Optimized deployment for computing and storage

Awesome, so let us get started!!

How do Large Language Models work?

The power of LLMs (Large Language Models) stems from a transformer-based architecture, which was detailed in the seminal paper Attention is All You Need. This architecture enables the model to understand not just individual words, but also their context within a sentence by assigning attention weights to each word. Since machine learning models process numbers rather than words, the first step involves converting words into numerical vectors, known as tokenization.

Multi-Head Attention

The transformer architecture features a multi-head attention mechanism that operates in parallel, allowing for independent attention to different aspects of the data. This layer enhances the model’s ability to focus on varied nuances in the text, thereby extracting richer contextual meanings.

After the application of the attention layer, the data is processed through a fully connected feed-forward network. The resulting output is a vector of logits proportional to the likelihood of each token in the tokenizer’s dictionary. These logits are then normalized via a softmax layer.

The selection of tokens for generating responses is influenced by various methods, such as adjusting the model's temperature settings in platforms like GPT.

Token Selection Techniques in Text Generation

• Greedy Decoding: As suggested by the name, this method selects the token with the highest probability. While it often produces coherent responses, it may also lead to repetitive text.
• Random Sampling: This approach randomly selects tokens, with the degree of randomness adjustable through parameters like Temperature, TopK, and TopP, allowing for a balance between randomness and precision in the generated text.

Let us understand how we can control the randomness of the text generated:

Temperature:

• This measures the randomness of the content generated. The higher the temperature, the greater the creativity and randomness in the content. Temperature is a scaling factor that is applied in the final softmax layer and impacts the shape of the probability distribution of the next token generation, as illustrated in the image below.
• This temperature variation distributes the probability such that at lower temperatures, the probability is peaked on specific tokens, while at higher temperatures, the probability distribution is more flat.

TopK:

• This method is an improvement over random sampling, which ensures that only the top “k” number of values are selected.

TopP

• This method is another enhancement over random sampling. It ensures that the total probabilities of tokens selected for random sampling do not exceed a threshold "p". For example, if there are 5 tokens with varying probabilities and TopP = p, the model will ensure that for the token selected, the sum (P1 + P2 + ... + P5) ≤ p.

Now, it is not necessary that all Generative AI models will always have an encoder and a decoder layer. Encoder-decoder models work best for sequence-to-sequence operations like translation. Examples of various types of models:

• Encoder-Only Model: These types of models work well when the input and output are of the same length. They are particularly useful in processes like classification. Example: BERT
• Encoder-Decoder Model: In these types of models, the input and output lengths can vary, making them well-suited for text generation and translation tasks. Example: BART, T5
• Decoder Only Model: These models can be generalized to perform a variety of tasks and include some popular models like GPT, and Bloom.

I hope this blog gave you a good insight into the workings of Large Language Models and how to control text generation using various parameters. In the next blog in this series, we will deep dive into Deployment, Usage, and Prompt Engineering.

Thank you for following my blog and stay tuned for more informative content. If you would like to engage in a discussion on the use of Generative AI in industrial applications like Manufacturing and Finance, do connect with me. I have developed some great solutions at InsightAI that bring deep insights from your data. Connect with me at [email protected].

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