Essential guide to AI & ML Glossary
This glossary contains definitions of terms I use in my work. It is not comprehensive, but contains terms I use frequently. The definitions are meant to be useful in applying AI not perfect academic definitions.
A
Agent
AIAI system that can make decisions based on a given set of instructions. The degree of freedom can vary from a very structured task to a very open-ended one.
C
Continued Pre-training // Domain Adaptation
AITaking a large amount of documents from a specific area, in a specific language to change the semantics of the tokens. Often used in domains like law, where the tokens (words) mean different things than in the general lingo.
I
Instruction Fine-tuning
AIFine-tuning an LLM on a specific task with a small dataset of annotated examples.
L
Large Language Model (LLM)
AIA type of AI model trained on vast amounts of text data to understand and generate human-like text.
P
Pairwise ranking
AIScores two outputs against each other. For example, two documents are given and the model has to decide which one is better.
Pointwise ranking
AIScores a single output against a single reference. This is the most common type of evaluation. For example, a query and a document are given and the model has to score how well the document matches the query.
Pre-training
AITraining on vast amounts of data to learn patterns and relationships. LLMs are pre-trained on nearly the entire internet to learn linguistic patterns.
R
Reinforcement Learning by Human Feedback (RLHF)
AIUsing feedback data by humans (e.g. clicks, likes) to train a model that can label the output as good or bad and in turn using this to train the model.
T
Transformer
AIAn architecture for neural networks that uses self-attention mechanisms to process sequential data.