Semantic Softmax aggregates probabilities from semantic synonyms around target labels to correct renormalization bias in zero-shot LLM classification, lowering calibration error and raising AUROC and F1.
Proceedings of the 34th International Conference on Machine Learning , pages =
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Neural feature maps create expressive kernels that enable fast, scalable, and consistent exact Gaussian process inference for regression and classification.
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The Silent Vote: Improving Zero-Shot LLM Reliability by Aggregating Semantic Neighborhoods
Semantic Softmax aggregates probabilities from semantic synonyms around target labels to correct renormalization bias in zero-shot LLM classification, lowering calibration error and raising AUROC and F1.
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Scalable Gaussian process inference via neural feature maps
Neural feature maps create expressive kernels that enable fast, scalable, and consistent exact Gaussian process inference for regression and classification.