Sequence Transduction: The Forgotten Problem That Led to Modern LLMs

Sequence transduction, which involves transforming data from one form to another, played a pivotal role in the development of modern large language models (LLMs) that we use today. Initially, the focus wasn't on generating text but on tasks like translating languages and converting speech to text. This foundational work laid the groundwork for the sophisticated LLMs we see now. Understanding this history provides insight into how advancements in AI have evolved, emphasizing that the journey to today's capabilities was far from straightforward.