Large Language models or LLMs are Artificial Intelligence(AI) software that uses machine learning and other models to generate and recognize text and similar content. They use neural network models called transformation models that can learn context and meaning by tracking relationships in sequential data. They are trained in large columns of data with millions or billions of parameters.
Why is the Large Language Model(LLM) Important?
LLMs are flexible models that can perform various tasks, such as listed below.
- Summarizing Documents
- Translating Languages
- Completing sentences
- Answering Questions
They can take a few prompts and inputs and make predictions based on that. They use an Artificial Intelligence(AI) model to produce content based on feedback provided in human language.
How does the Large Language Model(LLM) Work?
In LLM models, multidimensional vectors are used to represent words so that words with similar contextual meanings or relationships are close to each other in vector space.
The previous form of the Machine learning model uses a numerical table to represent each word. But the caveat of this representation is that it could not recognize the relationship between words with similar meaning. Transformers uses these word embeddings to pre-process the text as numerical representation through encoder and understand the context of words and phases with similar meanings, as well as other relationship between words and part of the speech. Once this knocked is obtained, LLMs applies this through a decoder to produce a unique output.
What are examples of LLM Models?
Below are some of the examples of LLM Models
- Open AI GPT-3 Model
- AI21 Lab’s Jurasic-1 Model
- LightOne’s Paradigm Models
