The LLM & agent era
What made GPT-3 a turning point?
GPT-3 showed that a single large model could pick up a new task from a few examples written into the prompt, with no retraining. That shifted how people used language models.
In May 2020, OpenAI released a paper called Language Models are Few-Shot Learners, describing GPT-3, a model with 175 billion parameters. That was about ten times larger than any comparable model before it.[1]
The headline was not just the size. Before GPT-3, using a model for a new task usually meant fine-tuning it, which is extra training on labeled examples for that specific job. GPT-3 showed you could often skip that step. You write a few examples of the task directly into the prompt, and the model picks up the pattern and continues it. The paper applied the model to many tasks this way, “without any gradient updates or fine-tuning,” specifying the task purely through text.[1]
This is called few-shot learning in context, sometimes shortened to in-context learning. One model, frozen after training, could be steered toward translation, question answering, or simple arithmetic just by how you wrote the prompt. That changed the relationship between people and models. Instead of training a new model per task, you described the task in words. It is the habit that the chat products built on a couple of years later would make ordinary.
References
- Language Models are Few-Shot Learners — arXiv (Brown et al., OpenAI)