LLM Models Basics and BERT vs. GPT: Two Titans of Natural Language Processing

LLM refers to Language Learning Model, a general term that can be applied to any machine learning model that is designed to understand, generate, and manipulate human language. The term LLM isn’t a specific model or architecture but can describe a wide range of models from simpler ones like Naive Bayes classifiers used in sentiment analysis, to complex ones like Transformer-based models like BERT, GPT, and T5.

The terms “encoder”, “decoder”, and “encoder-decoder” are often used to describe the architecture of certain types of machine learning models, particularly those used in natural language processing (NLP). Here’s what they mean:

1. Encoder-only models: These models, like BERT (Bidirectional Encoder Representations from Transformers), are designed to understand the context of words in a sentence by considering both the left and the right context of a word. They are typically used in tasks where the entire context of the input needs to be understood, such as text classification, named entity recognition, and extractive question answering.
2. Decoder-only models: These models, like GPT (Generative Pretrained Transformer), generate text sequentially from left to right and are typically used in language generation tasks. They are “autoregressive”, meaning that they…