Intuitive explanations of the most well-liked LLMs
In my last article, we dived into the world of machine learning models, understanding their working principles and the way they fit into various practical applications.
Today, we’ll enterprise into something that has quite literally taken over the whole tech space, large language models. Specifically, we’re going to undergo several of essentially the most influential language models in use as of 2023.
With that said, let’s dive into it?
Before we dive in, large language models may be generally classified into three categories based on their architecture:
- Transformer-based models
- RNN-based models
- Other modern architectures
These models leverage the ability of attention mechanisms to process language data. Popular transformer-based models include GPT-4, BERT, RoBERTa, and T5
GPT-4
GPT-4 uses the transformer architecture with a specific emphasis on the self-attention mechanism to capture the contextual relationship between words in a sentence regardless of their positions. Its “masked” training methodology allows the model to generate highly coherent and contextually relevant text.
- Pro: Highly expert at generating coherent and contextually relevant text.
- Con: As a generative model, it might create plausible-sounding but factually incorrect or misleading information.
- Useful for: Text generation tasks, conversation agents, content creation.
BERT
BERT uses bidirectional transformers, meaning it processes input data from each left-to-right and right-to-left. This bidirectional context gives BERT a deeper understanding of the meaning of every word in a sentence and the way they relate to one another, greatly enhancing its performance on tasks like query answering and sentiment evaluation.