From the 1 of 29 linked papers with an AI index.
1 citations · 1 across the 14 of their papers we have counts for
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Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning
Julia Witte Zimmerman, Denis Hudon, Kathryn Cramer +9
Tokenization is a necessary component within the current architecture of many language mod-els, including the transformer-based large language models (LLMs) of Generative AI, yet i…
Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance
P. S. Dodds, T. Alshaabi, M. I. Fudolig +7
From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arous…
Statistical laws and linguistics differ in naturalistic video and fictional conversations
Ashley M. A. Fehr, Calla G. Beauregard, Julia Witte Zimmerman +4
Conversation is a cornerstone of social connection and is linked to well-being outcomes. Conversations vary widely in type with some portion generating complex, dynamic stories. On…
LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti
Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds
Large Language Models (LLMs) have demonstrated strong translation abilities through prompting, even without task-specific training. However, their effectiveness in dialectal and lo…
A suite of allotaxonometric tools for the comparison of complex systems using rank-turbulence divergence
Jonathan St-Onge, Ashley M. A. Fehr, Carter Ward +6
Describing and comparing complex systems requires principled, theoretically grounded tools. Built around the phenomenon of type turbulence, allotaxonographs provide map-and-list vi…