Introduction to Large Language Models
LLMs - Overview:
Advanced AI models creating human-like text.
Trained on extensive text and code datasets for diverse applications.
Ability to understand and generate text akin to human quality.
Benefits of LLMs:
Generation of high-quality, human-like text.
Versatility across multiple tasks and domains.
Trained on extensive and diverse datasets.
Continuously evolving and improving over time.
LLMs - Applications:
Writing, translation, and coding tasks.
Answering queries in natural language.
Summarizing large volumes of text efficiently.
Challenges with LLMs:
Risk of generating harmful or biased content.
High expenses associated with training and maintenance.
Potential for biases reflected in the generated output.
An Introduction to Large Language Models
"Introduction to Large Language Models" delves into the realm of LLMs, including its significance and applications. The course, led by Google Cloud's John Ewald, covers critical topics such as:
Definition of Large Language Models (LLMs):
LLMs are a subset of Deep Learning that is a component of Generative AI.
They create fresh content such as text, photographs, music, and video.
They specifically refer to wide, versatile language models that have been broadly taught and fine-tuned for certain uses.
The Primary Features of LLMs:
Large Scale:
They operate on massive datasets and parameter counts, learning massive amounts of data during training.
General-Purpose:
Suitable for common linguistic tasks in a variety of sectors.
Fine-tuned and pre-trained: Initially taught generally, then refined for specific applications using smaller datasets.
The Advantages of Large Language Models:
Versatility:
Multiple tasks, such as translation, categorization, and question answering, can be addressed by a single model.
Minimal Field Training:
Can perform well despite having minimal domain-specific training data.
Continuous Improvement:
With more data and settings, performance improves.
The PaLM Model: Example
Google's 540 billion-parameter PaLM model excels in a variety of language tasks, taking advantage of the Pathways system to efficiently train across several TPU v4 Pods.
Traditional Programming vs. Machine Learning:
Traditional ML vs. LLM Development:
LLMs necessitate quick design rather than specialist knowledge and considerable training.
Example of a Use Case - Text Generation:
Answering questions without domain expertise is possible using generative QA.
Prompt Design & Engineering:
In NLP, quick engineering improves performance with specific approaches.
Types of Large Language Models:
Generic Language Models, Instruction Tuned Language Models, and Dialog Tuned Language Models all require different prompting approaches.
Tuning for Specific Tasks:
Vertex AI provides task-specific foundation models for sentiment analysis, vision tasks, and other applications, enabling for customisation based on use cases.
Methods for Efficient Tuning:
Parameter-Efficient Tuning Methods allow you to tune individual add-on layers without having to change the main model.
Generative AI Studio and the PaLM API:
Platforms for exploring, customizing, and deploying generative AI models on Google Cloud.
Introduction to Large Language Models by Google Cloud
This course provides an in-depth overview of the potential and practical applications of Large Language Models, as well as insights into their variety and customization options.
Coursera: Link
