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A single Noise Sensitivity Exponent (NSE) dictates when learning becomes computationally intractable in high-dimensional single- and multi-index models.
Temperature scaling, the go-to method for uncertainty control, is formally proven to increase classifier uncertainty, but its impact on LLM diversity is questioned, revealing nuances in its application.
