Prueba de modelado del lenguaje

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. En la formulación del modelo de lenguaje autorregresivo planteado, ¿qué se predice a partir del contexto simbólico anterior?
   • A probability distribution for the next token
   • The original file's alphabetical rank
   • Only a temperature measurement from the device
   • A mandatory image label with no sequence information
2. ¿Qué limitaciones deberían considerarse para un modelo de lenguaje de n-gramas de historia corta?
   • A requirement that all n-gram models reject every unseen sequence even with smoothing
   • A guarantee of unlimited history
   • A requirement to process only images
   • Limited context and possible zero probabilities for unseen continuations without smoothing
3. ¿Por qué el entrenamiento previo del modelo de lenguaje del siguiente token puede obtener objetivos sin una anotación de clase separada para cada posición?
   • The model has no training objective
   • The target next tokens are already present in the observed text
   • All training text must have been generated by the same model
   • Every position requires an external image classifier
4. ¿Cómo utiliza la generación de texto autorregresivo un token de continuación seleccionado?
   • It predicts every future token independently in one step
   • It discards every selected token before the next prediction
   • It appends the token and uses available earlier output as context for later predictions
   • It changes the training labels to future deployment outcomes
5. ¿Qué objetivo se minimiza comúnmente para la configuración de entrenamiento de siguiente token autorregresivo indicada?
   • Squared numerical distance between alphabetical token ID values
   • Only the number of tokens in an output filename
   • Only hinge loss on image-pixel locations
   • Cross-entropy or negative log-likelihood for observed target tokens
6. What do temperature and top-p choices affect during sampled language-model generation?
   • How continuation tokens are selected from model scores
   • The number of trained layers after every token
   • A guarantee that all output claims become true
   • The label written in every observed training sequence
7. During teacher-forced next-token training, where does the preceding context for a target token come from?
   • Only the model's freely sampled earlier output
   • A randomly generated replacement corpus at every position
   • The observed training sequence
   • The final evaluation labels supplied after deployment
8. A stated two-token prefix has probabilities 0.5 for the first token and 0.2 for the second given the first. What is its joint prefix probability?
   • 0.7
   • 0.1
   • 0.4
   • 0.3