# LLM - Large Language Models

# What is a Large Language Model?

A large language model is a model that is trained on a large corpus of text. The model is then able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on. The model is able to generate text that is similar to the text it was trained on.

# Examples of Large Language Models

OpenAI's GPT (Generative Pre-trained Transformer) series is the most well-known example of an autoregressive language model. GPT-4 is the latest and most powerful iteration of this series.
BERT: Bidirectional Encoder Representations from Transformers, developed by Google, is one of the most famous autoencoding language models. It can be fine-tuned for a variety of NLP tasks, such as sentiment analysis, named entity recognition, and question answering.
T5: T5 is a combination of autoencoding and autoregressive models. LLMs rely on complex algorithms including transformer architectures that shift through large datasets and recognize patterns at the word level. This data helps the model better understand natural language and how it is used in context and then make predictions related to text generation, text classification, etc. There are three main types of large language models (LLMs) based on the transformer architecture: autoencoding, autoregressive, and combination models.
Autoregressive Language Models (e.g., GPT)
Autoencoding Language Models (e.g., BERT)
Combination of autoencoding and autoregressive such as T5 model.
LLMs are powerful tools for processing natural language data quickly and accurately with minimal human intervention. However, LLMs have some limitations, including :

High computational and storage requirements: LLMs require huge amounts of computational resources and storage to train and run. Limited ability to understand context: LLMs may not always understand the context of the text they are processing, leading to errors in analysis and generation. Biases in training data: LLMs can learn biases from the training data, which can lead to biased results. Despite these limitations, LLMs are essential components for today's AI applications, and NLP researchers and specialists should familiarize themselves with large language models if they want to stay ahead in this rapidly evolving field.

# References

https://www.ibm.com/cloud/learn/large-language-models