Results for "probability correctness"

Describes likelihoods of random variable outcomes.

Constraining outputs to retrieved or provided sources, often with citation, to improve factual reliability.

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

Probabilities do not reflect true correctness.

Mathematical guarantees of system behavior.

Samples from the k highest-probability tokens to limit unlikely outputs.

Probability of data given parameters.

Stochastic generation strategies that trade determinism for diversity; key knobs include temperature and nucleus sampling.

Measures how one probability distribution diverges from another.

Models that define an energy landscape rather than explicit probabilities.

Penalizes confident wrong predictions heavily; standard for classification and language modeling.

Updating beliefs about parameters using observed evidence and prior distributions.

Estimating parameters by maximizing likelihood of observed data.

Graphical model expressing factorization of a probability distribution.

Average value under a distribution.

Samples from the smallest set of tokens whose probabilities sum to p, adapting set size by context.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samples.

A measure of randomness or uncertainty in a probability distribution.

Measures divergence between true and predicted probability distributions.

Measures how much information an observable random variable carries about unknown parameters.

Probability of treatment assignment given covariates.

Two-network setup where generator fools a discriminator.

Sample mean converges to expected value.

Variable whose values depend on chance.

Sampling from easier distribution with reweighting.

Predicting borrower default risk.

Scalar summary of ROC; measures ranking ability, not calibration.

When information from evaluation data improperly influences training, inflating reported performance.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).