Introduction to Large Language Models
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John Ewald from Google Cloud explains that large language models (LLMs) are a subset of deep learning, intersecting with generative AI. LLMs are versatile models trained for general language tasks and can be fine-tuned for specific applications. The concept is broken down into three key features: "Large" refers to both the extensive training data and parameter count, "general purpose" indicates their suitability for common language problems, and "pre-trained and fine-tuned" describes the process of initially training on a broad dataset and then refining for specific tasks using smaller datasets. The benefits of using such models are highlighted. [Introduction to Large Language Models](https://www.youtube.com/watch?v=zizonToFXDs) ## Benefits of using Large Language Models - **Versatility**: Large language models (LLMs) can handle diverse tasks, such as translation, sentence completion, text classification, and question answering, thanks to their extensive training with petabyte-scale data and billions of parameters. - **Minimal Training Data**: LLMs can be tailored for specific tasks using only a small amount of domain-specific training data. They perform well even with limited data, supporting few-shot and zero-shot scenarios. - **Continuously Improving Performance**: The performance of LLMs keeps improving as more data and parameters are added. An example is Google's PaLM, a 540 billion-parameter model, released in April 2022, which excels in various language tasks. - **Example Model**: The introduction mentions "PaLM," short for "Pathways Language Model," a dense decoder-only [transformer model](/posts/Transformers-The-best-idea-in-AI-Andrej-Karpathy-and-Lex-Fridman), released by Google as a high-performance, large-scale language model. ## Pathways Language Model (PaLM) - **Parameters and Architecture**: The PaLM (Pathways Language Model) contains 540 billion parameters and employs the pathways system, allowing efficient training across multiple TPU V4 pods. This new AI architecture can handle various tasks simultaneously, learn new tasks quickly, and improve its understanding of the world. - **Pathway Architecture**: The pathway architecture enables PaLM to coordinate distributed computation for accelerators and supports efficient task handling and learning. - **Transformer Model**: PaLM is a [transformer model](posts/Transformers-Explained-Understand-the-model-behind-GPT-BERT-and-T5), comprising an encoder and decoder. The encoder processes input sequences, while the decoder decodes representations for specific tasks. - **Evolution of AI Approaches**: The evolution from traditional programming to [neural networks](/posts/What-Is-a-Neural-Network) to generative models is highlighted. Traditional programming required hard-coded rules, neural networks enabled image recognition, and [generative models](/posts/Introduction-to-Generative-AI) like PaLM and LaMDA allow content generation based on large internet-sourced data. - **User Interaction**: Models like PaLM and LaMDA can be used by users to generate content through prompts, whether typed or spoken, providing comprehensive information based on their learned data. - **Comparison with LLM Development**: The introduction teases a forthcoming comparison of Large Language Model (LLM) development using pre-trained models. ## LLM Development vs. Traditional Development - **LLM Development vs. Traditional Development**: LLM development contrasts with traditional ML development. In LLM development, expertise, training examples, and model training are not required. Instead, emphasis is on designing clear, concise, and informative prompts. This is different from traditional ML, which demands training examples and computing resources. - **Text Generation Use Case - Question Answering (QA)**: QA involves automatically answering questions in natural language. Traditional QA systems require domain knowledge for model development, while generative QA models generate free text responses directly from context, without the need for domain expertise. - **Generative QA with Bard**: Bard, a Google AI-developed large language model chatbot, showcases generative QA. It provides detailed responses to questions like calculating net profit, fulfilling order quantities, and determining average sensors per region. - **Importance of Prompt Design**: The success of responses is attributed to effective [prompt design](/posts/What-is-Prompt-Tuning) and prompt engineering, highlighting their crucial role in obtaining desired model outputs. ## What are Prompts and Prompt Engineering? - **Prompts and Prompt Engineering**: [Prompts and prompt engineering](/posts/What-is-Prompt-Tuning) are closely related concepts in natural language processing, involving creating clear and informative prompts. Key differences exist between the two. - **Prompt Design**: Involves [tailoring a prompt](/posts/What-is-Prompt-Tuning) to a specific task, like specifying translation from English to French for an English input. - **Prompt Engineering**: Aims to enhance performance by utilizing domain-specific knowledge, providing output examples, or using effective keywords. - **General vs. Specialized**: [Prompt design](/posts/What-is-Prompt-Tuning) is general, while prompt engineering is specialized and essential mainly for high-performance systems. - **Types of Large Language Models**: - **Generic Language Models**: Predict the next word based on training data language, similar to autocomplete in search. - **Instruction Tuned**: Trained to respond to specific instructions, such as summarizing text, generating poems, or classifying text sentiment. - **Dialogue Tuned**: Trained for dialogue with responses, often framed as questions to a chatbot. Works well in back-and-forth conversations with natural phrasing. ## Chain of Thought Reasoning - **Chain of Thought Reasoning**: This concept involves models being more accurate when they provide an explanation for their answer. They perform better by first outputting text that justifies the answer. - **Example Scenario**: A math question about tennis balls is presented without an immediate answer. Initially, the model might struggle to provide the right answer directly. - **Progressive Improvement**: As additional information or questions are given, the model's responses become more likely to include the correct answer due to the chain of thought reasoning. - **Practical Limitations**: Models that attempt to do everything might have limitations. Task-specific tuning can enhance Large Language Models (LLMs) reliability by focusing their capabilities. ## Model Garden Task Specific Models - **Model Garden Task-Specific Models**: Vertex AI offers task-specific foundation models tailored for various use cases. For example, sentiment analysis for gauging customer feelings or specific models for vision tasks and occupancy analytics. - **Tuning a Model**: Tuning allows customization of model responses based on task examples, adapting it to new domains or custom use cases through training on fresh data. - **Domain-Specific Tuning**: You can fine-tune a model for specific domains, like legal or medical, by training on relevant data. - **Fine Tuning**: Fine-tuning involves adjusting model weights for your data, requiring a substantial training effort and potentially hosting your own model. - **Medical Foundation Model**: An example of a medical foundation model, trained on healthcare data, handles tasks like question answering, image analysis, and patient similarity. - **Efficient Tuning Methods**: More efficient tuning methods exist beyond fine-tuning to make the process less resource-intensive. ## More efficient methods of tuning - **Parameter-Efficient Tuning Methods (PETM)**: These methods allow tuning a large language model on custom data without altering the base model. A few add-on layers are tuned, which can be swapped during inference. - **Generative AI Studio**: Facilitates exploration and customization of generative AI models for Google Cloud applications. Provides pre-trained models, tools for fine-tuning and deploying models, and a developer forum for collaboration. - **Generative AI App Builder**: Enables code-free creation of Gen AI apps using a drag-and-drop interface, visual editor, built-in search engine, and conversational AI engine. Suitable for chatbots, digital assistants, search engines, knowledge bases, and more. - **PaLM API**: Allows testing and experimentation with Google's large language models and Gen AI tools. Can be integrated with Maker Suite for graphical interface access, including model training, deployment, and monitoring tools. - **Maker Suite Tools**: Comprises a model-training tool, model-deployment tool, and model-monitoring tool. Supports various algorithms for model training, multiple deployment options, and performance monitoring with dashboards and metrics. - **Course Conclusion**: This video concludes the course on Introduction to Large Language Models. If you enjoyed this, checkout [A Hackers Guide to Large Language Models](/posts/A-Hackers-Guide-to-Large-Language-Models)