Plain-language definitions for AI and LLM terms — from attention mechanisms to zero-shot prompting.
An LLM-powered system that can take actions, use tools, and pursue multi-step goals autonomously without human input at each step.
A technique that allows each token in a sequence to 'pay attention' to all other tokens, enabling the model to understand context and relationships across long distances.
AI-assisted software development where the AI operates autonomously across multiple steps — reading files, executing code, observing results, and iterating — with minimal human intervention per action.
Mathematical functions like ReLU, GELU, or SwiGLU that introduce nonlinearity into neural networks, enabling them to learn complex patterns beyond linear relationships.
Systems that enable AI agents to store, retrieve, and use information across multiple interactions or sessions, extending beyond the model's context window.
AIME (American Invitational Mathematics Examination) problems are challenging high-school competition math questions used to evaluate advanced mathematical reasoning capabilities in AI models.
The process of ensuring AI models behave according to human values and intentions through training techniques, evaluation methods, and safety measures.
The AI2 Reasoning Challenge (ARC) is a benchmark dataset of grade-school science questions designed to test AI models' reasoning abilities.
A text generation approach where models produce sequences one token at a time, with each new token conditioned on all previously generated tokens.
182 terms · Updated regularly