Analyzing limits for in-context learning
This addresses a foundational problem in understanding transformer capabilities for researchers, revealing incremental insights into their limitations.
The paper challenges prior claims that transformers implement standard learning algorithms during in-context learning, providing empirical evidence and mathematical analysis showing they cannot achieve general predictive accuracy due to architectural limitations.
Our paper challenges claims from prior research that transformer-based models, when learning in context, implicitly implement standard learning algorithms. We present empirical evidence inconsistent with this view and provide a mathematical analysis demonstrating that transformers cannot achieve general predictive accuracy due to inherent architectural limitations.