Last Updated on July 17, 2023 by Editorial Team
Author(s): Jair Ribeiro
Originally published on Towards AI.
Uncovering the Implicit Implementation of Standard Learning Algorithms in Neural Sequence Models.
During my Sunday reading this week, I found this research paper which explores the hypothesis that transformer-based neural sequence models can implicitly implement standard learning algorithms during in-context learning.
In-context learning is a unique way for language models to learn and perform tasks by only looking at examples of inputs and outputs without making any changes to their internal workings.
It is related to the process in that the language model discovers hidden concepts from the data it was previously trained on. And even when the outputs are random, the language model can still use all other parts of the example (inputs,… Read the full blog for free on Medium.
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Published via Towards AI