Differences Between GPT and Traditional Neural Network Models (RNN)

GPT differs significantly from traditional Recurrent Neural Network (RNN) models in terms of structure, training methods, and overall performance.

In this lesson, we will explore how GPT and RNN differ and in what ways GPT exhibits superior performance.

RNNs are neural network structures specialized for processing data where order is important, such as text.

They read sentences one word at a time, incorporating information from previously seen words into the prediction of the next word.

For example, if given the sentence "I am eating," an RNN would process 'I am' → 'eating' one word at a time, predicting the next word in sequence.

While RNNs are suited to sequential data, they are prone to suffer from the long-term dependency problem, where they may forget earlier information as the sentence gets longer.

GPT is a language model based on the transformer architecture.

Utilizing the Self-Attention mechanism, the core of transformers, it processes the entire sentence at once and efficiently learns the relationships between words.

Unlike RNNs, it does not process data sequentially. Instead, all words reference each other simultaneously to understand the meaning of the sentence.

This allows GPT to effectively comprehend long sentences and complex contexts.

Additionally, GPT is pre-trained on large-scale datasets and has a versatile structure applicable to a range of tasks, enabling it to perform a variety of language tasks using a single model.

Sentence: "The cat climbed the tree. And it made a sound."

• Because RNN reads words in order, it may lose track that 'it' refers to 'the cat.'

• GPT, viewing the entire sentence at once, can accurately associate 'it' with 'the cat.'

GPT is faster and more advanced than RNNs, and it adapts easily to many language tasks.

It is particularly advantageous in tasks requiring comprehension of long sentences or complex contexts.

In the next session, we will delve into tokens, the units of input for the GPT model.