How language models scale with model size, training data, and training compute
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The world of artificial intelligence is witnessing a revolution, and at its forefront are large language models that seem to grow more powerful by the day. From BERT to GPT-3 to PaLM, these AI giants are pushing the boundaries of what’s possible in natural language processing. But have you ever wondered what fuels their meteoric rise in capabilities?
In this post, we’ll embark on a fascinating journey into the heart of language model scaling. We’ll uncover the secret sauce that makes these models tick — a potent blend of three crucial ingredients: model size, training data, and computational power. By understanding how these factors interplay and scale, we’ll gain invaluable insights into the past, present, and future of AI language models.
So, let’s dive in and demystify the scaling laws that are propelling language models to new heights of performance and capability.
Table of content: This post consists of the following sections:
- Introduction
- Overview of recent language model developments
- Key factors in language model scaling