The shift to prompt-based approaches
As discussed in prior chapters, the development of the original GPT marked a significant advance in natural language generation, introducing the use of prompts to instruct the model. This method allowed models such as GPT to perform tasks such as translations – converting text such as “Hello, how are you?” to “Bonjour, comment ça va?” – without task-specific training, leveraging deeply contextualized semantic patterns learned during pretraining. This concept of interacting with language models via natural language prompts was significantly expanded with OpenAI’s GPT-3 in 2020. Unlike its predecessors, GPT-3 showcased remarkable capabilities in understanding and responding to prompts in zero- and few-shot learning scenarios, a stark contrast to earlier models that weren’t as adept at such direct interactions. The methodologies, including the specific training strategies and datasets used...
