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Generative AI


This guide introduces basic concepts of generative artificial intelligence (AI) and how it could be applied in teaching, learning, and research. It provides resources related to prompt engineering techniques which are key to effective human-generative AI interaction. The guide also offers guidance on using generative AI ethically and how to cite AI-generated content. Additional resources include CMU guidelines and learning materials related to generative AI.

"CMU mascot Scotty is reading a book with a computer in the library" generated by Gemini

Generative AI Glossary

Auto-Regressive Model: "A model that infers a prediction based on its own previous predictions. For example, auto-regressive language models predict the next token based on the previously predicted tokens. All Transformer-based large language models are auto-regressive."

Chain-of-Thought Prompting: "A prompt engineering technique that encourages a large language model (LLM) to explain its reasoning, step by step."

Chat: "The contents of a back-and-forth dialogue with an ML system, typically a large language model. The previous interaction in a chat (what you typed and how the large language model responded) becomes the context for subsequent parts of the chat."

Contextualized Language Embedding: "An embedding that comes close to "understanding" words and phrases in ways that native human speakers can. Contextualized language embeddings can understand complex syntax, semantics, and context."

Context Window: "The number of tokens a model can process in a given prompt. The larger the context window, the more information the model can use to provide coherent and consistent responses to the prompt."

Distillation: "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."

Few-Shot Prompting: "A prompt that contains more than one (a "few") example demonstrating how the large language model should respond."

Fine Tuning: "A second, task-specific training pass performed on a pre-trained model to refine its parameters for a specific use case."

Instruction 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."

Low-Rank Adaptability: "An algorithm for performing parameter efficient tuning that fine-tunes only a subset of a large language model's parameters."

Model Cascading: "A system that picks the ideal model for a specific inference query."

Model Router: "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."

One-shot prompting: "A prompt that contains one example demonstrating how the large language model should respond."

Parameter-Efficient Tuning: "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."

Pre-Trained Model: "Models or model components (such as an embedding vector) that have already been trained."

Pre-Training: "The initial training of a model on a large dataset."

Prompt: "Any text entered as input to a large language model to condition the model to behave in a certain way."

Prompt-based Learning: "A capability of certain models that enables them to adapt their behavior in response to arbitrary text input (prompts)."

Prompt Engineering: "The art of creating prompts that elicit the desired responses from a large language model."

Prompt Tuning: "A parameter efficient tuning mechanism that learns a "prefix" that the system prepends to the actual prompt."

Reinforcement Learning from Human Feedback: "Using feedback from human raters to improve the quality of a model's responses."

Role Prompting: "An optional part of a prompt that identifies a target audience for a generative AI model's response."

Soft Prompt Tuning: "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."

Temperature: "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."

Zero-Shot Prompting: "A prompt that does not provide an example of how you want the large language model to respond."


Definitions from Google Generative AI Glossary:

Machine Learning Glossary: Generative AI. Google for Developers. Retrieved May 10, 2024, from