In-Context Learning with Transformer-Based Neural Sequence Models.

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.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI