U-Space: Uncovering When and Why Uncertainty Arises in Language Models
U-Space maps language-model residual states into a subspace that tracks token-level doubt and certainty without extra training.
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U-Space maps language-model residual states into a subspace that tracks token-level doubt and certainty without extra training.
XConf estimates LLM confidence from accumulated past episodes, improving calibration and discrimination across nine benchmarks at one-tenth self-consistency cost.
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