A Closer Look at In-Context Learning under Distribution Shifts
Kartik Ahuja David Lopez-Paz
In-context learning, a capability that enables a model to learn from input
examples on the fly without necessitating weight updates, is a defining
characteristic of large language models. In this work, we follow the setting
proposed in (Garg et al., 2022) to better understand the generality and
limitations of in-context learning from the lens of the simple yet fundamental
task of linear regression. The key question we aim to address is: Are
transformers more adept than some natural and simpler architectures at
performing in-context learning under varying distribution shifts? To compare
transformers, we propose to use a simple architecture based on set-based
Multi-Layer Perceptrons (MLPs). We find that both transformers and set-based
MLPs exhibit in-context learning under in-distribution evaluations, but
transformers more closely emulate the performance of ordinary least squares
(OLS). Transformers also display better resilience to mild distribution shifts,
where set-based MLPs falter. However, under severe distribution shifts, both
models' in-context learning abilities diminish.