引导式测验 · 中等 等级
受约束和语法指导的生成测验
查看正式的解码规则和解析器状态,然后通过 JSON 和屏蔽采样示例评估完成条件和概率变化。
问题 1 的 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
- 实现代币级约束的主要技术难点是什么?
- 模型内存立即耗尽
- 分词器边界可能与语法边界不一致
- 无法为 JSON 编写语法
- GPU 无法进行矩阵乘法
- 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