MLPs Learn In-Context on Regression and Classification Tasks

ICLR 2025 Conference Submission1336 Authors

Keywords: In-context learning, relational reasoning, synthetic tasks, MLP, MLP-Mixer, Transformer

TL;DR: On a range of widely studied synthetic in-context learning tasks, we find that MLPs perform comparably with Transformers under the same compute budget.

Abstract: In-context learning (ICL), the remarkable ability to solve a task from only input exemplars, is often assumed to be a unique hallmark of Transformer models. By examining commonly employed synthetic ICL tasks, we demonstrate that multi-layer perceptrons (MLPs) can also learn in-context. Moreover, MLPs, and the closely related MLP-Mixer models, learn in-context *competitively with Transformers given the same compute budget* in this setting. We further show that MLPs *outperform* Transformers on a series of classical tasks from psychology designed to test relational reasoning, which are closely related to in-context classification. These results underscore a need for studying in-context learning beyond attention-based architectures, while also challenging strong prior arguments about MLPs' limited ability to solve relational tasks. Altogether, our results highlight the unexpected competence of MLPs, and support the growing interest in all-MLP alternatives to task-specific architectures.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 1336