Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability

• URL: http://arxiv.org/abs/2310.08049v3
• Date: Tue, 2 Apr 2024 01:54:53 GMT
• Title: Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning Ability
• Authors: Ivan Lee, Nan Jiang, Taylor Berg-Kirkpatrick,
• Abstract summary: We evaluate 13 models capable of causal language modeling across a suite of synthetic in-context learning tasks. All the considered architectures can perform in-context learning under a wider range of conditions than previously documented. Several attention alternatives are sometimes competitive with or better in-context learners than transformers.
• Score: 39.42414275888214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: What is the relationship between model architecture and the ability to perform in-context learning? In this empirical study, we take the first steps toward answering this question. We evaluate thirteen model architectures capable of causal language modeling across a suite of synthetic in-context learning tasks. These selected architectures represent a broad range of paradigms, including recurrent and convolution-based neural networks, transformers, state space model inspired, and other emerging attention alternatives. We discover that all the considered architectures can perform in-context learning under a wider range of conditions than previously documented. Additionally, we observe stark differences in statistical efficiency and consistency by varying the number of in-context examples and task difficulty. We also measure each architecture's predisposition towards in-context learning when presented with the option to memorize rather than leverage in-context examples. Finally, and somewhat surprisingly, we find that several attention alternatives are sometimes competitive with or better in-context learners than transformers. However, no single architecture demonstrates consistency across all tasks, with performance either plateauing or declining when confronted with a significantly larger number of in-context examples than those encountered during gradient-based training.

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