Cuestionario vinculado a una guía · duro Nivel

Prueba de colapso del modelo

Pruebe el entrenamiento recursivo, la cobertura de casos raros, la procedencia de los datos, la evaluación independiente y por qué los resultados de los datos sintéticos dependen de la configuración.

Rutas de guía relacionadasColapso del modelo
Pregunta 1 de 8

In the guide’s description, what process can cause model collapse?

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  1. In the guide’s description, what process can cause model collapse?
    • Training only on independently collected real-world data
    • Training later models mainly on samples generated by earlier models
    • Testing a model on data it did not generate
    • Adding a few selected synthetic examples while retaining the original data
  2. Which part of the distribution can be especially easy to lose during recursive training?
    • Every common pattern at once
    • Only the labels used in validation
    • Rare cases and low-frequency patterns
    • The examples that were held out from training
  3. Why should a team keep an independently sourced evaluation set?
    • It guarantees that all synthetic data are accurate
    • It makes provenance records unnecessary
    • It ensures every model will improve
    • It can reveal coverage loss without repeating the generator’s own omissions
  4. When testing synthetic augmentation, what comparison does the guide recommend?
    • A real-data baseline, the proposed mixture, and relevant alternatives under the same budget
    • Only the largest synthetic dataset available
    • A model trained on generated data with no evaluation set
    • A synthetic model against a different task and compute budget
  5. What should a team record about generated training material?
    • Only the total number of rows
    • Only the final benchmark score
    • Its provenance and how it was generated
    • Only the model’s name
  6. What does the guide conclude about synthetic data in general?
    • It always causes collapse
    • Its effect depends on the data mixture, generation, filtering, training, and evaluation setup
    • It always improves coverage
    • Its effect depends only on model size
  7. In the guide’s invented 90-to-10 example, why does the next sample matter?
    • It proves a real model will lose exactly 80 percent of rare examples
    • It shows all synthetic data are biased
    • It measures the compute needed for training
    • A lower share of rare examples can change the balance represented in later training
  8. What conclusion does the guide say a useful synthetic-data experiment should report?
    • Which tested conditions improved or degraded, with uncertainty and setup described
    • That every model will inevitably collapse
    • Only the highest average accuracy
    • A prediction about all future AI systems