Introduction to Large Language Models

Presenter Information

• Name: John Ewald
• Position: Training Developer at Google Cloud

Course Overview

• Define large language models (LLMs).
• Describe LLM use cases.
• Explain prompt tuning.
• Describe Google's gen AI development tools.

What Are Large Language Models?

• Definition: Large general-purpose language models that can be pre-trained and fine-tuned for specific purposes.
• Relation to Generative AI:
  • Generative AI produces new content (text, images, audio, synthetic data).

Key Features of Large Language Models

1. Size:
   • Enormous training datasets, sometimes at the petabyte scale.
   • High parameter counts often termed hyperparameters, which define a model's problem-solving skills.
2. General Purpose:
   • Can solve common language problems across various industries (e.g., text classification, question answering).
3. Pre-training and Fine-tuning:
   • Pre-training involves training a model with a large dataset for general purposes.
   • Fine-tuning tailors the model to specific problems with smaller datasets.

Benefits of Large Language Models

• Multiple Task Use:
  • A single model can perform various tasks (e.g., language translation, text classification).
• Minimal Data Requirement:
  • Effective with little domain-specific data (few-shot or zero-shot learning).
• Continuous Performance Improvement:
  • Performance improves with more data and parameters.

Example: Palm Model

• Release Date: April 2022
• Description:
  • 540 billion parameters model achieving state-of-the-art performance in various language tasks.
  • Utilizes the Pathways system for efficient multi-task training.

Transformer Model Architecture

• Components:
  • Encoder: Encodes the input sequence.
  • Decoder: Learns to decode for relevant tasks.

Evolution in AI Models

• Transition from traditional programming (hard-coded rules) to neural networks and generative models.
• Users can generate content simply by using prompts.

Differences Between LLM Development and Traditional ML Development

• LLM Development:
  • No expertise required, not reliant on training examples.
  • Focus is on prompt design for natural language processing tasks.
• Traditional ML Development:
  • Requires training examples, compute time, and hardware.

Use Case Example: Question Answering (QA)

• QA systems automatically answer questions posed in natural language based on context.
• Generative QA models do not require domain knowledge; they generate answers based on inputs.

Prompt Design vs. Prompt Engineering

• Prompt Design:
  • Creating tailored prompts for specific tasks.
• Prompt Engineering:
  • Improving model performance through strategic prompt crafting (e.g., using domain-specific knowledge).

Types of Large Language Models

1. Generic Language Models:
   • Predict the next word based on training data (like autocomplete).
2. Instruction Tuned Models:
   • Trained to follow specific instructions (e.g., summarizing text).
3. Dialogue Tuned Models:
   • Designed for conversational contexts, improving interactions based on dialogue.

Chain of Thought Reasoning

• Observations show that models perform better when initial text outputs explain reasoning before answering.

Task-Specific Tuning

• Task-specific models can improve reliability (e.g., sentiment analysis, occupancy analytics).
• Fine-tuning involves training models on new data for specific domains.

Parameter Efficient Tuning Methods (PETM)

• Allows tuning LLMs on custom data without altering the base model.

Google Cloud Tools

• Generative AI Studio:
  • Provides tools for creating and deploying generative AI models.
• Gen AI App Builder:
  • Drag-and-drop interface for building apps without coding.
• Palm API:
  • APIs for experimenting with Google’s language models and tools.

Conclusion

• Understanding LLMs equips users to leverage AI for practical applications across industries.

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Thanks for watching this course introduction to large language models!