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The "T" in GPT stands for this neural network architecture introduced in 2017 that revolutionized natural language processing through self-attention mechanisms.
- Tensor
- Transformer
- Transfer
- Token
When you type a message to an AI assistant, each word or word-piece is converted into one of these basic units that the model actually processes.
- Embeddings
- Neurons
- Parameters
- Tokens
This technique uses human ratings of AI responses to train a reward model, which then guides the AI to produce more helpful and safer outputs.
- RLHF
- RAG
- LoRA
- PEFT
This company, founded in 2021 by former members of the ChatGPT team, created Claude and focuses on AI safety research.
- OpenAI
- DeepMind
- Anthropic
- Cohere
The maximum amount of text a model can process at once, measured in tokens, is called this—and it determines how much conversation history the model can "remember."
- Batch size
- Embedding dimension
- Learning rate
- Context window
This phenomenon occurs when a language model confidently generates false or fabricated information that sounds plausible but has no basis in reality.
- Hallucination
- Overfitting
- Underfitting
- Catastrophic forgetting
These numerical representations convert words into dense vectors, capturing semantic relationships so that "king" and "queen" are mathematically closer than "king" and "banana."
- One-hot vectors
- Embeddings
- Gradients
- Activations
This training objective, used by GPT models, involves predicting the next word in a sequence given all the previous words.
- Autoregressive modeling
- Masked language modeling
- Contrastive learning
- Sequence-to-sequence
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