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GPTs are transformer-based large language models trained via generative pre-training on unlabeled data and then adapted for tasks.
A generative pre-trained transformer (GPT) is a large language model built on the transformer neural network architecture. GPTs are trained in two main stages: first, they undergo generative pre-training on large amounts of unlabeled data to learn general language patterns by predicting tokens in sequence; then, they are adapted to specific tasks using additional training (such as fine-tuning). Because of this setup, GPTs can generate novel text and, in many modern variants, other modalities like images and audio. GPTs became widely influential after OpenAI introduced GPT-1 in 2018, followed by GPT-2 (2019) and GPT-3 (2020), which scaled model size and data and improved capabilities such as coherent generation and few-shot/zero-shot task performance. After GPT-3, OpenAI used reinforcement learning from human feedback (RLHF) to better align model outputs with human preferences, leading to InstructGPT and then ChatGPT (late 2022). The broader GPT ecosystem expanded as other organizations developed their own GPT-like models, including open-weight and multimodal systems. Beyond the basic idea, the topic also covers how GPT performance relates to scaling laws (how model size, data, and compute affect outcomes), how “emergent abilities” can appear only at larger scales (e.g., in-context learning and multi-step reasoning), and how transformer efficiency techniques address the high computational cost of standard attention. Finally, it highlights evaluation/benchmarking practices and ethical concerns such as bias, misinformation, and environmental impact from training large models.
GPTs are transformer-based large language models trained via generative pre-training on unlabeled data and then adapted for tasks.
OpenAI’s GPT-1, GPT-2, and GPT-3 demonstrated major gains through scaling, while RLHF helped align outputs and enabled instruction-following systems like ChatGPT.
Performance is influenced by scaling laws and training balance (not just parameter count), and larger models may show emergent abilities such as in-context learning.
Transformer efficiency methods (e.g., sparse or memory-optimized attention) help reduce compute/memory costs for longer contexts.
Evaluation uses benchmarks and metrics for accuracy plus robustness and other properties, while ethical issues include bias, misinformation, and environmental costs.
A self-supervised training stage where a model learns to generate data patterns from large unlabeled datasets before being adapted to tasks.
A deep learning model design that uses attention mechanisms to process sequences efficiently and supports large-scale language modeling.
A transformer variant used by GPT models that focuses on generating the next token in a sequence.
Additional training on labeled data to adapt a pre-trained model to a specific task or objective.
The ability to perform tasks using only a small number of examples provided in the prompt.
The ability to perform a task without task-specific training examples, relying on learned generalization from pre-training.
A post-training method that uses human preference signals to align model behavior with what humans prefer.
The ability to learn and perform tasks from examples included directly in the prompt without additional model training.
Capabilities that appear only when models reach sufficient scale and are not present in smaller versions trained similarly.
Empirical relationships describing how model performance changes with factors like model size, dataset size, and compute.
The standard attention cost grows roughly with the square of sequence length, motivating efficiency improvements for long contexts.
Testing GPTs on datasets and metrics to measure performance across tasks and properties such as robustness and bias.
The generation of plausible but inaccurate or misleading information, often because the model optimizes fluency rather than factual verification.
The energy and resource costs associated with training large models, motivating more efficient training and reporting.
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