Locality, relation, and meaning construction in language, via tokenization in LLMs
Abstract
Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. We examine the details of their gnogeography. We present high-level takeaways on cognition, language, and the future of learning machines as well as the science around them, arguing that relation is the fundamental mechanism for linguistic meaning construction, and that problem-solving is fundamentally achieved through subverting locality via the mechanism of relation.
Primary Faculty Mentor Name
Chris Danforth
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Data Science
Primary Research Category
Social Science
Locality, relation, and meaning construction in language, via tokenization in LLMs
Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. We examine the details of their gnogeography. We present high-level takeaways on cognition, language, and the future of learning machines as well as the science around them, arguing that relation is the fundamental mechanism for linguistic meaning construction, and that problem-solving is fundamentally achieved through subverting locality via the mechanism of relation.
