A research lab is developing a new large-scale language model. They have access to a state-of-the-art neural architecture designed to effectively process long sequences of text. They are debating between two training strategies:

1. Strategy A: Train the model from scratch on a high-quality, human-labeled dataset of 1 million examples specifically designed for question-answering.
2. Strategy B: First, train the model on a massive, unlabeled corpus of 1 trillion words from the internet with the objective of predicting the next word in a sentence. Then, optionally, adapt it to specific tasks.

Which strategy is more likely to produce a powerful, general-purpose model capable of a wide range of language understanding and generation tasks, and why?

The Engine of Modern AI: Architecture and Learning

The development of powerful, general-purpose language models was significantly accelerated by a key combination of an architectural innovation and a learning strategy. Which statement best analyzes the distinct yet complementary roles of these two components in this breakthrough?