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A Guide to Large Language Model Abstractions

A Guide to Large Language Model Abstractions

The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.

Insights by Peter Yong Zhong (Carnegie Mellon), Haoze He (Carnegie Mellon), Omar Khattab (Stanford), Christopher Potts (Stanford), Matei Zaharia (Berkeley), Heather Miller (Two Sigma, Carnegie Mellon)
January 16, 2024

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