Cuestionario vinculado a una guía · Medio Nivel
Prueba de generación restringida y guiada por gramática
Revise las reglas de decodificación formales y el estado del analizador, luego evalúe las condiciones de finalización y los cambios de probabilidad a través de JSON y ejemplos de muestreo enmascarado.
Pregunta 1 de 8
At which stage does the described hard grammar constraint restrict a language model's continuation choices?
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- At which stage does the described hard grammar constraint restrict a language model's continuation choices?
- Only while collecting pretraining documents
- Only after executing every emitted instruction
- Decoding, before the next token is selected
- By retraining the model weights after every user request
- Which formal rule representations are discussed in the cited grammar-guided generation research?
- Only image-file color palettes
- Only a spreadsheet of model parameter counts
- Only an alphabetical list of document filenames
- Regular expressions and context-free grammars
- Why is a simple finite-state machine not a complete description of every general nested context-free grammar decoder?
- General nested structure can require stack-like or richer parser state
- All nested grammars must be regular expressions by definition
- A grammar can never describe nesting
- Every parser must discard its current state after each token
- ¿Cuál es la principal dificultad técnica al implementar restricciones a nivel de token?
- El modelo se queda sin memoria al instante.
- Es posible que los límites del tokenizador no se alineen con los límites gramaticales
- Las gramáticas no se pueden escribir para JSON
- Las GPU no pueden multiplicar matrices
- What structure claim is justified for an output that successfully completes under a correctly implemented supported grammar constraint?
- Every fact inside it is necessarily true
- Every possible interruption leaves a complete accepted output
- Every downstream application rule is automatically covered
- It satisfies the constraints covered by that grammar
- Which named research engine studies flexible and efficient structured generation for language models?
- A photo-upscaling file format
- An audio loudness unit
- XGrammar
- A required model-weight encryption algorithm
- Generation stops after only an opening JSON object brace. What can be concluded about that text?
- It must already be a complete JSON object
- It can be a permitted prefix without being a complete valid object
- The interruption proves no JSON grammar exists
- Every such prefix is necessarily forbidden from the start
- A stated distribution assigns A 0.2, B 0.3, and C 0.5. After C is masked and the remaining choices are renormalized, what is A's probability?
- 0.4
- 0.2
- 0,5
- 0.6