LLM stands for Large Language Model, which is a type of artificial intelligence algorithm that uses deep learning techniques and massive data sets to understand and generate text similar to how humans communicate. LLMs are trained on a huge amount of text data, typically petabytes in size, to produce human-like responses to natural language inputs. They are used in a variety of consumer and business applications, such as question answering, information retrieval, sentiment analysis, and more.
Some key features of LLMs include:
- Data set size: LLMs typically have at least one billion or more parameters, and are trained on a large volume of data that is typically petabytes in size.
- Word representation: LLMs represent words in a way that recognizes relationships between them, such as words with similar meanings.
- Fine-tuning: This is an extension of few-shot learning in which data scientists train a base model to adjust its parameters with additional data relevant to the specific application.
While LLMs have many possible uses and are demonstrating remarkable abilities to make predictions based on a relatively small number of prompts or inputs, they also have limitations. One limitation is their lack of interpretability, as LLMs are quite complex algorithms and deep learning in general is often referred to as a "black box". Another limitation is the potential for bias in the input data, which can lead to discriminatory outputs from the AI and reinforce existing societal inequalities.
