A Beginner's Guide to Large Language Models

Large Language Models (LLMs) are a type of deep learning model designed to process and understand vast amounts of natural language data. Built on neural network architectures, particularly the transformer architecture, LLMs have revolutionized the field of natural language processing. In this presentation, we will explore the world of LLMs, their significance, and the different types of LLMs based on the transformer architecture, such as autoregressive language models (e.g., GPT), autoencoding language models (e.g., BERT), and combined models (e.g., T5). Join us as we delve into the world of LLMs and discover their potential in shaping the future of natural language processing.

1. What are Large Language Models? https://vitalflux.com 5/1/2023 https://vitalflux.com 1

2. Topics Introduction Transformer architecture Different types of large language models Autoregressive Language Models (e.g., GPT) Autoencoding Language Models (e.g., BERT) Combination of Autoregressive and Autoencoding Models (e.g., T5) Conclusion 5/1/2023 https://vitalflux.com 2

3. Introduction 5/1/2023 https://vitalflux.com 3

4. Transformer Architecture Introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017 Represents the neural network model for natural language processing tasks Consists of two main components: the encoder network and the decoder network The key component of the transformer architecture is the self-attention mechanism, which enables the model to attend to different parts of the input sequence to compute a representation for each position 5/1/2023 https://vitalflux.com 4

5. Different Types of LLMs 5/1/2023 https://vitalflux.com 5

6. Autoregressive Language Models (e.g., GPT) Generate text by predicting the next word in a sequence given the previous words Trained to maximize the likelihood of each word in the training dataset, given its context 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 the GPT series 5/1/2023 https://vitalflux.com 6

7. Autoencoding Language Models (e.g., BERT) Learn to generate a fixed-size vector representation of input text by reconstructing the original input from a masked or corrupted version of it Trained to predict missing or masked words in the input text by leveraging the surrounding context BERT (Bidirectional Encoder Representations from Transformers), developed by Google, is one of the most famous autoencoding language models Can be fine-tuned for a variety of NLP tasks, such as sentiment analysis, named entity recognition, and question answering 5/1/2023 https://vitalflux.com 7

8. Combination of Autoregressive and Autoencoding Models (e.g., T5) Combines both autoregressive and autoencoding models T5 model (Text-to-Text Transfer Transformer) can perform both text generation and text understanding tasks Can be fine-tuned for a wide range of NLP tasks, such as machine translation, summarization, and question answering 5/1/2023 https://vitalflux.com 8

9. Conclusion LLMs have revolutionized the field of natural language processing Transformer architecture has played a crucial role in enabling this advancement Autoregressive, autoencoding, and combined models are the three main types of LLMs based on the transformer architecture https://vitalflux.com/large-language-models-concepts-examples/ 5/1/2023 https://vitalflux.com 9