Domain 2: Gen AI Fundamentals Flashcards
____ is a subset of deep learning. Like deep learning, this is a multipurpose technology that helps to generate new original content rather than finding or classifying existing content.
Generative AI
_____ looks for statistical patterns in modalities, such as natural language and images.
Gen AI foundational models
_____ are very large and complex neural network models with billions of parameters that are learned during the training phase or pre-training.
Gen AI foundational models
The more parameters a model has, the more _____ it has, so the model can perform more advanced tasks.
memory
Gen AI models are built with _____, _____, _____,and_____ all working together.
neural networks, system resources, data, and prompts
The current core element of generative AI is the _____.
transformer network
_____ are pre-trained on massive amounts of the text data from the internet, and they can use this pre-training process to build up a broad knowledge base.
Large Language Models (LLMs)
A ____is a natural language text that requests the generative AI to perform a specific task.
prompt
The process of reducing the size of one model (known as the teacher) into a smaller model (known as the student) that emulates the original model’s predictions as faithfully as possible.
distillation
A prompt that contains more than one (a “few”) example demonstrating how the large language model should respond. For example, the following lengthy prompt contains two examples showing a large language model how to answer a query.
few-shot prompting
A second, task-specific training pass performed on a pre-trained model to refine its parameters for a specific use case.
fine tuning
A form of fine-tuning that improves a generative AI model’s ability to follow instructions. Instruction tuning involves training a model on a series of instruction prompts, typically covering a wide variety of tasks. The resulting instruction-tuned model then tends to generate useful responses to zero-shot prompts across a variety of tasks.
instruction tuning
An algorithm for performing parameter efficient tuning that fine-tunes only a subset of a large language model’s parameters.
Low-Rank Adaptability (LoRA)
A system that picks the ideal model for a specific inference query.
model cascading
The algorithm that determines the ideal model for inference in model cascading. A model router is itself typically a machine learning model that gradually learns how to pick the best model for a given input. However, a model router could sometimes be a simpler, non-machine learning algorithm.
model router
A prompt that contains one example demonstrating how the large language model should respond. For example, the following prompt contains one example showing a large language model how it should answer a query.
one-shot prompting
A set of techniques to fine-tune a large pre-trained language model (PLM) more efficiently than full fine-tuning. Parameter-efficient tuning typically fine-tunes far fewer parameters than full fine-tuning, yet generally produces a large language model that performs as well (or almost as well) as a large language model built from full fine-tuning.
parameter-efficient tuning
Models or model components (such as an embedding vector) that have been already been trained. Sometimes, you’ll feed pre-trained embedding vectors into a neural network. Other times, your model will train the embedding vectors themselves rather than rely on the pre-trained embeddings.
pre-trained model
The initial training of a model on a large dataset. Some pre-trained models are clumsy giants and must typically be refined through additional training. For example, ML experts might pre-train a large language model on a vast text dataset, such as all the English pages in Wikipedia.
pre-training
Any text entered as input to a large language model to condition the model to behave in a certain way. Prompts can be as short as a phrase or arbitrarily long (for example, the entire text of a novel). Prompts fall into multiple categories, including those shown in the following table:
prompt
A capability of certain models that enables them to adapt their behavior in response to arbitrary text input (prompts). In a typical prompt-based learning paradigm, a large language model responds to a prompt by generating text. For example, suppose a user enters the following prompt:
prompt-based learning
The art of creating prompts that elicit the desired responses from a large language model. Humans perform prompt engineering. Writing well-structured prompts is an essential part of ensuring useful responses from a large language model. Prompt engineering depends on many factors, including:
prompt engineering
Using feedback from human raters to improve the quality of a model’s responses. For example, an RLHF mechanism can ask users to rate the quality of a model’s response with a 👍 or 👎 emoji. The system can then adjust its future responses based on that feedback.
Reinforcement Learning from Human Feedback (RLHF)
An optional part of a prompt that identifies a target audience for a generative AI model’s response. Without a role prompt, a large language model provides an answer that may or may not be useful for the person asking the questions. With a role prompt, a large language model can answer in a way that’s more appropriate and more helpful for a specific target audience.
role prompting
A technique for tuning a large language model for a particular task, without resource intensive fine-tuning. Instead of retraining all the weights in the model, soft prompt tuning automatically adjusts a prompt to achieve the same goal.
soft prompt tuning
A hyperparameter that controls the degree of randomness of a model’s output. Higher temperatures result in more random output, while lower temperatures result in less random output.
temperature
A prompt that does not provide an example of how you want the large language model to respond. For example:
zero-shot prompting
The process that a trained machine learning model uses to draw conclusions from brand-new data.
inference
When the purpose of the prompt is finished.
completion
The amount of textual information that the AI can take into account at any given time when processing language
context window
The smallest units of text that an AI model processes.
tokens
This breaks down text into these units to make it manageable for computational models.
tokenizer
Providing examples inside the context window is called _____. With this, you can help LLMs learn more about the task being asked by including examples or additional data in the prompt.
in-context learning
The input that you sent into your generative model is called the _____, which consists of instructions and content.
prompt
T/F: The larger a model is, the more likely it is to work without additional in-context learning or further training. Because the model’s capability increases with size, it has supported the development of larger and larger models.
True
What’s the result of a model’s capability increasing with size?
- Access to enormous amounts of data for training
- Development of more powerful compute resources
- Introduction of highly scalable transformer architecture
LLMs encode a deep statistical representation of a language. This understanding is developed during the _____ phase when the model learns from vast amounts of unstructured data.
pre-training
