A Closer Look at In-Context Learning under Distribution Shifts

• URL: http://arxiv.org/abs/2305.16704v1
• Date: Fri, 26 May 2023 07:47:21 GMT
• Title: A Closer Look at In-Context Learning under Distribution Shifts
• Authors: Kartik Ahuja, David Lopez-Paz
• Abstract summary: We aim to better understand the generality and limitations of in-context learning from the lens of the simple yet fundamental task of linear regression. We find that both transformers and set-based distributions exhibit in-context learning under-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 distributions falter.
• Score: 24.59271215602147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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