Guide-linked quiz · Medium Level

Constrained and Grammar-Guided Generation Quiz

Review formal decoding rules and parser state, then assess completion conditions and probability changes through JSON and masked-sampling examples.

Question 1 of 8

At which stage does the described hard grammar constraint restrict a language model's continuation choices?

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  1. 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
  2. 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
  3. 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
  4. What is the main technical difficulty in implementing token-level constraints?
    • The model runs out of memory instantly
    • Tokenizer boundaries may not line up with grammar boundaries
    • Grammars cannot be written for JSON
    • GPUs cannot multiply matrices
  5. 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
  6. 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
  7. 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
  8. 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

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