A Tutorial on Large Language Models: Understanding the Basics

🔑Large language models are probalistic models of documents, predicting the probability of word tokens.

💡An autoregressive language model predicts the next token based on the previous tokens.

🌍Perplexity measures the average number of bits needed to communicate the next word in a language model.

🤔Language models make assumptions about fixed history and a categorical distribution of word predictions.

🔬Perplexity has been a useful metric for language modeling, enabling comparison between different models.

What is perplexity in language modeling?

—Perplexity is a metric that quantifies how well a language model predicts the next word in a sequence of words.

How are large language models trained?

—Large language models are trained using neural networks that learn to predict the probability distribution of the next word based on the previous words.

What are the challenges in language modeling?

—Challenges in language modeling include handling the zipfian distribution of words, determining the optimal length of binary codes for words, and choosing an appropriate training corpus.

Can language models be used for tasks other than language generation?

—Yes, language models have been used for a variety of tasks, including machine translation and text completion.

How can perplexity be improved in language models?

—Perplexity can be improved by fine-tuning the model on specific tasks, using larger training datasets, and incorporating more contextual information.

00:00The tutorial introduces large language models and presents five core formulas to understand their behavior.

04:17The first section explores generation and introduces perplexity as a metric for evaluating language models.

09:32Assumptions about fixed history and categorical word distributions are discussed, along with the conversion of probability distributions to binary codes.

11:47Perplexity is explained in more detail and its correlation with downstream performance is highlighted.

12:59The tutorial concludes by discussing how language models can be used for various tasks and potential strategies for improving perplexity.