Results for "generated samples"

Model-generated content that is fluent but unsupported by evidence or incorrect; mitigated by grounding and verification.

Learning from data generated by a different policy.

Artificial sensor data generated in simulation.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

Ordering training samples from easier to harder to improve convergence or generalization.

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

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

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

Models that learn to generate samples resembling training data.