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LLMs have a fundamental Shannon capacity: scaling model size or data without preserving a sufficient signal-to-noise ratio inevitably degrades performance.
LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.
