A monolingually trained linear probe on intermediate LLM representations predicts answer correctness zero-shot across typologically diverse languages, with confidence signals concentrated in middle layers.
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Perceptual geometry for color, pitch, emotion and taste emerges transiently in intermediate layers of transformer LLMs despite purely textual training.
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Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models
A monolingually trained linear probe on intermediate LLM representations predicts answer correctness zero-shot across typologically diverse languages, with confidence signals concentrated in middle layers.