Understanding Large Language Models

The video explains how large language models function as sophisticated mathematical tools that predict the next word in a text. It describes the process of building a chatbot by setting up an interaction between a user and an AI assistant, where the model predicts responses. Instead of predicting a single word with certainty, the model assigns probabilities to possible next words, allowing for more natural and varied outputs. This approach means that the same prompt can yield different responses each time.

What is the primary function of a large language model?

• To translate languages

• To perform mathematical calculations

• To generate images

• To predict the next word in a text

In building a chatbot, what is the initial step involving text?

• Creating a database of responses

• Laying out text that describes an interaction

• Designing a user interface

• Programming the AI's personality

How does a large language model determine the next word in a sequence?

• By following a predefined script

• By using a fixed dictionary

• By selecting the most common word

• By assigning probabilities to possible next words

Why might a large language model choose less likely words in its predictions?

• To reduce processing time

• To make the output appear more natural

• To avoid repetition

• To increase randomness

What is a potential outcome of running the same prompt multiple times in a large language model?

• The same answer every time

• A shorter response each time

• A different answer each time

• A longer response each time