Decoding AI Jargons with Chai

GPT (Generative Pre-trained Transformer) models have revolutionized artificial intelligence by creating human-like text based on input prompts. This article breaks down complex terminology into simple explanations with practical examples to help you understand how these powerful AI systems work.

What is GPT?

GPT stands for Generative Pre-trained Transformer, a type of advanced AI model designed to understand and generate human-like text based on input prompts. As the name suggests, it consists of three core elements:

• Generative: It generates new content (text, code, images, etc.)

• Pre-trained: It learns from massive amounts of text data before being used, means it is being trained.

• Transformer: It uses a special architecture that helps it understand relationships between words and all the working of ai lies here.

GPT models have evolved through several versions (GPT-2, GPT-3, GPT-4, etc.), with each iteration offering greater capabilities and understanding.

The Basic Building Blocks of GPT

Transformers

It's the architecture that powers GPT models. Think of it as the engine that allows the AI to process and generate text.

In simple terms, transformers are components within AI models that process information by paying attention to relationships between different parts of the input data. Unlike earlier AI models that processed text sequentially (word by word), transformers can look at an entire text sequence at once and understand how each part relates to the others.

Encoder and Decoder

The original transformer architecture has two main components:

• Encoder: Converts human text into a form the AI can understand deeply.

  It's like a translator that takes your words and transforms them into a special AI language that captures the meaning and context.

• Decoder: Takes the AI's internal representations and converts them back into human-readable text.

  It's the reverse translator that produces the text you actually see.

From Words to Numbers: How GPT Processes Text

Tokenization and Vocabulary Size

Before an AI can work with text, it needs to break it down into manageable pieces:

Tokenization is the process of splitting text into smaller units called tokens. These can be words, parts of words, or even individual characters.

For example, the sentence "I love machine learning" might become tokens ["I", "love", "machine", "learning"].

Vocabulary Size refers to the total number of unique tokens the model knows. Think of this as the AI's dictionary - the larger it is, the more nuanced the AI's understanding can be.

Vectors and Embeddings

Once text is tokenized, it needs to be converted into numbers that the AI can process:

Vectors are lists of numbers that represent tokens mathematically. For instance, the word "king" might be represented as [0.2, 0.8, 0.1, 0.5][1].

Embeddings take these vectors and place them in a multi-dimensional space where similar words are positioned closer together. In this space:

• "Cat" and "kitten" would be near each other

• "Hot" and "cold" might be far apart

• "River" and "bank" might have a complex relationship

In short, this mathematical representation allows the AI to "understand" relationships between words.

Position Encoding

The transformer architecture processes all tokens simultaneously, which creates a problem: it could lose track of word order. Position encoding solves this by adding information about where each token appears in the text.

Consider the user's example:

• "Krunal loves Sachita but Sachita does not"

• "Sachita loves Krunal but Krunal does not"

These sentences contain identical words but have opposite meanings because of word order. Position encoding ensures the model understands this distinction by adding position information to each token's representation.

The Attention Mechanism: The Heart of GPT

Self-Attention

Self-attention is the revolutionary mechanism that allows GPT to understand context. For each word in a sentence, self-attention asks: "How much should I focus on every other word (including myself) to understand this word's meaning in this context?"

For example, in "The elephant couldn't cross the bridge because it was too heavy":

• When processing "it," the model needs to figure out what "it" refers to

• Through self-attention, it focuses heavily on "elephant" (rather than "bridge")

• This helps the model understand that "it" refers to "the elephant"

This mechanism is what gives GPT its impressive context awareness.

How Self-Attention Works

Self-attention operates through three key vectors created for each token:

1. Query vector: Represents what the current word is "asking about"

2. Key vector: Represents what other words "offer" in response

3. Value vector: Contains the actual information to be passed along

The process works like this:

1. Calculate attention scores between words by comparing queries and keys

2. Convert these scores to weights using the softmax function

3. Create a weighted sum of value vectors based on these weights

4. This produces a new representation of each word that incorporates context

Softmax Function

The softmax function converts a set of numbers into a probability distribution where all values are between 0 and 1 and sum to 1.

In self-attention, if the raw attention scores for a word with respect to three other words are [5.0, 2.0, 1.0], softmax might convert these to [0.8, 0.15, 0.05], indicating:

• The first word gets 80% of the attention

• The second word gets 15% of the attention

• The third word gets 5% of the attention

The name "softmax" comes from the fact that it's a "softer" version of simply taking the maximum value – it emphasizes the highest values while still considering others.

Multi-Head Attention

Multi-head attention runs the self-attention mechanism multiple times in parallel. Each "head" might learn to focus on different aspects of language:

• One might focus on subject-verb relationships

• Another might focus on pronouns and their referents

• Another might track temporal relationships

This is like having several people read the same text, each paying attention to different aspects, then combining their insights for a deeper understanding.

Important Generation Parameters

Temperature

Temperature controls the randomness of the model's outputs:

• Low temperature (0.2-0.5): More predictable, conservative outputs

• High temperature (0.8-1.0+): More random, diverse, and potentially creative outputs

Think of temperature as the "creativity dial" - higher settings produce more surprising and varied responses, while lower settings keep the AI more focused and predictable.

Knowledge Cutoff

The knowledge cutoff refers to the date until which the model has been trained on data. Models don't continuously learn from the internet - they have a specific cutoff date for their knowledge.

For example, if a model has a knowledge cutoff of January 2024, it won't know about events that happened after that date unless you tell it about them in your prompt.

Semantic Meaning

GPT's ability to understand semantic meaning involves recognizing the context and relationships between words rather than just analyzing words in isolation. It tries to predict the next word by understanding what makes sense conceptually in the given context.

For example, in "I went to the hospital because I was _____", the model understands that words like "sick," "injured," or "bleeding" would make semantic sense, while "happy" or "delicious" would not.

Conclusion

GPT models represent a remarkable achievement in artificial intelligence. By understanding key concepts like tokenization, embeddings, position encoding, and self-attention, we can better appreciate how these systems work.

At their core, GPT models learn patterns from vast amounts of text and use these patterns to predict what should come next, token by token. This approach, combined with the transformer architecture's attention mechanism, has created AI systems capable of producing human-like text that was unimaginable just a few years ago.

When working with these models, remember that they're not truly "understanding" text as humans do—they're making sophisticated predictions based on patterns they've observed. This knowledge helps us use these tools more effectively and responsibly in our writing, coding, and communication tasks.

To learn more about:

Attention Is All You Need - Whitepaper by Google - 2017