Introduction to Large Language Models (LLMs)

1. Overview of Large Language Models

• Large language models (LLMs) are a subset of deep learning.

• LLMs intersect with generative AI and are part of deep learning.

• Generative AI can produce new content, including text, images, audio, and synthetic data.

• LLMs are large general-purpose language models that can be pre-trained and fine-tuned for specific purposes.

• Pre-training involves training the model for general language problems, while fine-tuning tailors the model for specific tasks in different fields.

2. Key Features of Large Language Models

• Large indicates the enormous size of the training data set and the parameter count.

• General purpose means the models can solve common language problems across industries.

• Pre-trained and fine-tuned refers to the process of initially training the model on a large data set and then customizing it for specific tasks using a smaller data set.

3. Benefits of Using Large Language Models

• Single model versatility: A single model can be used for various tasks such as language translation, text generation, and question answering.

• Minimal field training data: Large language models can achieve decent performance even with limited domain-specific training data.

• Continuous improvement: The performance of large language models improves as more data and parameters are added.

4. Comparison with Traditional Machine Learning Development

• LLM development doesn't require expertise, extensive training examples, or model training. Prompt design plays a crucial role.

• Traditional machine learning development requires training examples, compute time, and hardware.

5. Text Generation Use Case: Question Answering

• Question answering (QA) is a subfield of natural language processing.

• QA systems trained on text and code can answer factual, definitional, and opinion-based questions.

• LLMs can generate free text responses based on the context, eliminating the need for domain-specific knowledge.

6. Prompt Design and Prompt Engineering

• Prompt design involves creating a clear, concise, and informative prompt tailored to a specific task.

• Prompt engineering focuses on improving performance by using domain-specific knowledge, providing desired output examples, or employing effective keywords.

• Prompt design is essential, while prompt engineering enhances accuracy and performance.

7. Types of Large Language Models

• Generic language models predict the next word based on training data and can be used for autocompletion.

• Instruction-tuned models predict responses based on given instructions.

• Dialogue-tuned models specialize in responding to questions in a conversational manner.

8. Chain of Thought Reasoning

• Models are more likely to give the correct answer when they first output text that explains the reason behind the answer.

9. Task-Specific Tuning and Parameter-Efficient Tuning

• Task-specific tuning allows customization of the model's response based on examples of the desired task.

• Fine tuning involves training the model on new data and adjusting all model weights.

• Parameter-efficient tuning methods (PETM) tune add-on layers without altering the base model, enabling customization without duplicating the model.

10. Generative AI Studio and App Builder

• Generative AI Studio provides tools and resources to explore and customize generative AI models, including pre-trained models, fine-tuning tools, and deployment options.

• Gen AI App Builder allows the creation of Gen AI apps without coding, using a drag-and-drop interface, visual editor, and conversational AI engine.

11. PaLM API and Maker Suite

• PaLM API allows testing and experimentation with Google's large language models and Gen AI tools.

• Maker Suite integrates with PaLM API, offering a graphical user interface for accessing the API, model training, deployment, and monitoring tools.

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