Title: Learning to Remember, Learn, and Forget in Attention-Based Models
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Attention Based Model, In-Context Learning, Continual Learning, Bayesian Inference, Metaplasticity.

Abstract: The ability to perform learning during inference,  i.e. in-context learning (ICL) is a core feature of self-attention in transformers. ICL acts like an online associative memory and is believed to underpin transformers' capabilities in complex sequence processing tasks. 
In some cases, ICL was shown to simulate online gradient descent of a local loss function on an input sequence.
In this work, we view ICL as a continual learning problem that may suffer from memory interference and requires a solution to a plasticity--stability dilemma. 
We examine here the memory consolidation properties of ICL and propose a Bayesian continual learning framework to solve this dilemma, leading to a new attention model. 
Our framework builds on the idea of metaplasticity in neuroscience, where the level of plasticity of each synapse is tied to an importance measure grounded by a Bayesian prior distribution capturing previously learned knowledge.
Our approach explains several gated linear attention models in the literature, identifying the respective assumptions from a Bayesian learning perspective.
Furthermore, our Bayesian continual learning approach provides a principled approach to forgetting, enabling the design of attention layers with a desired memory horizon.
Our experiments achieve competitive performances on  synthetic benchmarks. Additionally, we experiment on several commonsense reasoning benchmarks where small models benefit from consolidated synapses, outperforming strong baseline like Gated Delta Networks.

TL;DR: A Bayesian continual-learning framework for attention based model balances plasticity and stability, to achieve superior in-context learning.