A Beginner's Guide to Large Language Models

May 1, 2023Download as PPTX, PDF0 likes21,989 views

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.

What are Large
Language Models?
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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
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Introduction
LLMs are a type of
deep learning model
designed to process
and understand natural
language data
They are built on neural
network architectures,
particularly the
transformer
architecture
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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
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Different
Types of
LLMs
Autoregressive Language
Models (e.g., GPT)
Autoencoding Language
Models (e.g., BERT)
Combination of Autoregressive
and Autoencoding Models
(e.g., T5)
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Autoregressiv
e 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
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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
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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
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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
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This slide provides an overview of some of the core concepts related to building machine learning models. Machine learning is a branch of computer science that aims to make computers learn from data without being explicitly programmed. Learning problems can be classified into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves learning a function that maps inputs to outputs, given a set of labeled examples. Unsupervised learning involves finding patterns or structure in unlabeled data. Reinforcement learning involves learning how to act or behave in an environment, given feedback or rewards from the environment. Other important concepts related to machine learning include generalization, overfitting, representation, features, models, evaluation, optimization, bias-variance tradeoff, and Occam's razor. Generalization refers to the ability of a machine learning model to perform well on new or unseen data, not just on the training data. Overfitting occurs when a model fits the training data too closely, resulting in poor generalization. Representation refers to the way of encoding or describing the input and output data for a machine learning problem. Features are the attributes or characteristics of the input data that are used for learning. Models are the mathematical or computational structures that represent or approximate the function that maps inputs to outputs. Evaluation involves measuring the performance or accuracy of a machine learning model on a given data set. Optimization involves finding the best or optimal parameters or settings for a machine learning model that minimize the error or maximize the accuracy on the training data. Bias-variance tradeoff refers to the balance between model complexity and generalization ability. Occam's razor is a principle that favors simpler explanations or models when competing hypotheses explain the data equally well. Understanding these core concepts is crucial for anyone who wants to learn and apply machine learning in practice. This slide provides a concise summary of these concepts and can serve as a useful reference for beginners and experts alike.

How to Identify Analytics Use CasesAjitesh Kumar

Analytics is a powerful tool that can help organizations gain insights and make data-driven decisions. But with so much data available, it can be challenging to determine where to focus your efforts. This slide deck will provide you with a step-by-step guide on how to identify analytics use cases that can help you achieve your business goals. In this presentation, you will learn how to: Define your business goals and objectives Identify key performance indicators (KPIs) that measure progress towards your goals Gather and analyze data to identify patterns and trends Determine which use cases align with your business goals and KPIs Prioritize use cases based on potential impact and feasibility Develop a roadmap for implementing analytics use cases By following these steps, you will be able to identify the most valuable analytics use cases for your organization and create a plan to implement them successfully. Whether you are just starting with analytics or looking to expand your current capabilities, this presentation will provide you with the knowledge and tools you need to succeed.

What is first principles thinkingAjitesh Kumar

The document discusses first principles thinking, which involves breaking things down into their most basic elements or causes in order to innovate. There are four types of first causes according to Aristotle: final, formal, material, and efficient. Questioning techniques like the 5 Whys and Socratic method are key to understanding first principles. Examples of first principle thinkers include chefs, musicians, painters, and authors.

GPT-3 Models OverviewAjitesh Kumar

Generative AI Risks & ConcernsAjitesh Kumar

ChatGPT for Data Science ProjectsAjitesh Kumar

Machine Learning TerminologiesAjitesh Kumar

How to Identify Analytics Use CasesAjitesh Kumar

What is first principles thinkingAjitesh Kumar

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A Beginner's Guide to Large Language Models

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 LLMs are a type of deep learning model designed to process and understand natural language data They are built on neural network architectures, particularly the transformer architecture 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 Autoregressive Language Models (e.g., GPT) Autoencoding Language Models (e.g., BERT) Combination of Autoregressive and Autoencoding Models (e.g., T5) 5/1/2023 https://vitalflux.com 5

6. Autoregressiv e 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

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