História da GPT

The early GPT research sequence explored language-model pretraining, broader task transfer, and learning from examples supplied in context. This guide covers those research milestones, rather than presenting an exhaustive or current product-version list.

Principais conclusões

• Read milestones in their historical setting.
• Distinguish in-context examples from weight updates.
• Separate research models from the products built around them.

The 2018 work combined unsupervised language-model pretraining with supervised adaptation to language-understanding tasks. Its contribution concerned how a broadly pretrained transformer could support multiple downstream tasks with task-specific fine-tuning. The 2019 GPT-2 report examined language models as unsupervised multitask learners. It studied whether a next-token language model could perform tasks described through text without a separate training procedure for each task. The research framing matters: a result on a particular evaluation does not imply that every task is solved. The 2020 GPT-3 work emphasized few-shot evaluation. Examples were included in the input context, allowing the model to attempt a task without a gradient update for that individual task during the reported evaluation. This is different from fine-tuning model parameters on a labeled dataset. Keep research names, model versions, and products distinct. Chat interfaces, retrieval, tools, and later adaptation can change how a system behaves beyond its base language model. Historical results should be read with their datasets, prompts, evaluation settings, and limitations. They are evidence of a particular experiment rather than timeless measurements of current products.

1. Imagine a classifier prompted with three labeled examples before a fourth message. Its response changes, but no training job runs.
2. Describe this as an in-context example, not as a newly trained model.
3. If a separate job updates weights using many labeled messages, document the data and new model version as fine-tuning.

The constructed comparison helps avoid conflating two important ideas in GPT history.