2. Large Language Models

LLMs, like GPT-4, are advanced versions of these models. They are called “large” because they have a vast number of parameters (the weights the model learns during training).

For instance, GPT-3 has 175 billion parameters.

3. Transformers: The Backbone

The architecture that powers most LLMs is called the Transformer.

Introduced in 2017, Transformers use a mechanism called “attention” to process input data.

This allows the model to focus on different parts of the input sequence when making predictions, which is crucial for understanding context in language.

• Concept of Attention: The attention mechanism enables the model to weigh the importance of different words in a sentence. For example, in the sentence “The cat sat on the mat,” the word “cat” might be more important than “the” when predicting the next word.

• Self-Attention: Self-attention, also known as intra-attention, is a type of attention mechanism where a single sequence is compared with itself to find relationships between different positions. This is particularly useful for capturing long-range dependencies in text.

• Key Components: The self-attention mechanism involves three main components: Queries, Keys, and Values.

  Query (Q): Represents the current word or token for which we are calculating attention.

  Key (K): Represents all words or tokens in the sequence.

  Value (V): Represents the actual values of the words or tokens in the sequence.

• Applications in Transformers: In the Transformer architecture, self-attention is used in both the encoder and decoder layers. In the encoder, it helps to understand the input sequence, while in the decoder, it helps to generate the output sequence by focusing on relevant parts of the input.

4. Training Process

LLMs are trained using a method called unsupervised learning.

This involves feeding the model vast amounts of text data and letting it learn patterns and relationships between words and phrases without explicit labels.

The training process includes:

• Tokenization: Breaking down text into smaller units called tokens (e.g., words or subwords).

• Encoding: Converting these tokens into numerical representations (vectors).

• Attention Mechanism: Allowing the model to weigh the importance of different tokens in the input sequence.

• Decoding: Generating the output sequence based on the encoded input and learned patterns.

5. Word Vectors and Embeddings

Words are represented as vectors in a high-dimensional space. These vectors capture semantic meanings, so words with similar meanings have similar vector representations.

For example, the words “king” and “queen” might be close together in this space.

6. Challenges and Considerations

Despite their capabilities, LLMs have limitations and challenges:

• Bias: They can learn and propagate biases present in the training data.

• Resource Intensive: Training and running these models require significant computational resources.

• Interpretability: Understanding why a model makes a particular prediction can be difficult.

Capabilities and Limitations

While language models are powerful, they have limitations. They can generate coherent and contextually relevant text, but they don’t “understand” content as humans do. They can also perpetuate biases present in their training data.

Activity: Interacting with a Language Model

For this activity, you’ll interact with a language model.

Try asking it to summarize a complex article, translate a paragraph, or even write a poem. Observe its responses and note its strengths and limitations.

Remember, this may vary widely depending on the quality of the model you choose to interact with.