A Practical Introduction to Large Language Models (LLMs)

Shaw Talebi
22 Jul 202314:57

Summary

TLDRIn this data science series, Shah introduces large language models (LLMs), emphasizing their vast parameters and emergent properties like zero-shot learning. He explains the shift from supervised to self-supervised learning, highlighting next word prediction as the core task. Shah outlines three practical levels of LLM use: prompt engineering, model fine-tuning, and building your own LLM. The series aims to make LLMs accessible, with future videos covering APIs, open-source solutions, and practical applications.

Takeaways

  • 😀 Shah introduces a new data science series focused on large language models (LLMs) and their practical applications.
  • 🔍 The series will cover beginner-friendly introductions to LLMs, practical aspects, APIs, open-source solutions, fine-tuning, and building LLMs from scratch.
  • 🗣 Large language models, like Chat GPT, are advanced chatbots that can generate human-like responses to queries.
  • 📏 'Large' in LLM refers to the vast number of model parameters, ranging from tens to hundreds of billions, which define the model's functionality.
  • 🌟 A key qualitative feature of LLMs is 'emergent properties', such as zero-shot learning, which allows models to perform tasks without explicit training for those tasks.
  • 🔄 The shift from supervised learning to self-supervised learning in LLMs has been significant, with self-supervised learning relying on the structure within the data itself.
  • 🔮 The core task of LLMs is next word prediction, which they learn through exposure to massive amounts of text data, allowing them to understand word associations and context.
  • 🛠 Three levels of working with LLMs are discussed: prompt engineering (using LLMs out of the box), model fine-tuning (adjusting model parameters for specific tasks), and building your own LLM.
  • 💻 Prompt engineering can be done through user interfaces like Chat GPT or programmatically via APIs and libraries like OpenAI or Hugging Face Transformers.
  • 🔧 Model fine-tuning involves taking a pre-trained LLM and updating its parameters using task-specific examples, often resulting in better performance for specific use cases.
  • 🏗 For organizations with specific needs, building a custom LLM may be necessary, involving data collection, pre-processing, model training, and deployment.

Q & A

  • What is the main focus of Shah's new data science series?

    -The main focus of Shah's new data science series is to discuss large language models (LLMs) and their practical applications.

  • What is the difference between a large language model and a smaller one?

    -Large language models differ from smaller ones in two main aspects: quantitatively, they have many more model parameters, often tens to hundreds of billions; qualitatively, they exhibit emergent properties like zero-shot learning that smaller models do not.

  • What is zero-shot learning in the context of large language models?

    -Zero-shot learning refers to the capability of a machine learning model to complete a task it was not explicitly trained to do, showcasing an emergent property of large language models.

  • How does self-supervised learning differ from supervised learning in the context of large language models?

    -In self-supervised learning, models are trained on a large corpus of data without manual labeling, using the inherent structure of the data to define labels. This contrasts with supervised learning, which requires manually labeled examples for training.

  • What is the core task that large language models are trained to do?

    -The core task that large language models are trained to do is next word prediction, where they predict the probability distribution of the next word given the previous words.

  • What are the three levels of working with large language models mentioned by Shah?

    -The three levels of working with large language models mentioned by Shah are: 1) Prompt Engineering, 2) Model Fine-tuning, and 3) Building your own Large Language Model.

  • What is meant by prompt engineering in the context of large language models?

    -Prompt engineering refers to using a large language model out of the box, without altering any model parameters, and crafting prompts to elicit desired responses.

  • How does model fine-tuning in large language models work?

    -Model fine-tuning involves adjusting at least one internal model parameter of a pre-trained large language model to optimize its performance for a specific task using task-specific examples.

  • Why might an organization choose to build its own large language model?

    -An organization might choose to build its own large language model for security reasons, to customize training data, or to have full ownership and control over the model for commercial use.

  • What resources does Shah recommend for further exploration of large language models?

    -Shah recommends the blog in Towards Data Science and a GitHub repository for more details, example code, and further exploration of large language models.

Outlines

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Keywords

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Highlights

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Transcripts

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Étiquettes Connexes
Large Language ModelsData ScienceMachine LearningChat GPTPrompt EngineeringModel Fine-TuningSelf-Supervised LearningZero-Shot LearningHugging FaceOpenAI API
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