L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.
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Ensemble-based method of moments on softmax outputs produces stable Dirichlet predictive distributions that improve uncertainty-guided tasks like selective classification over evidential deep learning.
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
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Prior-Aligned Data Cleaning for Tabular Foundation Models
L2C2 is a deep RL framework that learns to clean tabular data by aligning it to the synthetic prior of tabular foundation models, yielding higher accuracy on some benchmarks and cross-dataset policy transfer.
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Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification
Ensemble-based method of moments on softmax outputs produces stable Dirichlet predictive distributions that improve uncertainty-guided tasks like selective classification over evidential deep learning.
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Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.