Token-Level Masking Technique
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A Token-Level Masking Technique is a constraint enforcement technique that modifies token probability distributions by masking invalid tokens during language model generation.
- AKA: Token Masking Technique, Logit Masking Technique, Vocabulary Masking Technique, Token Filtering Technique.
- Context:
- It can typically access Token-Level Masking Technique Logit Vectors from token-level masking technique language models before token-level masking technique softmax application.
- It can typically apply Token-Level Masking Technique Masks by setting token-level masking technique invalid token logits to token-level masking technique negative infinity.
- It can typically compute Token-Level Masking Technique Valid Token Sets based on token-level masking technique current context and token-level masking technique constraint rules.
- It can typically preserve Token-Level Masking Technique Probability Ratios among token-level masking technique valid tokens through token-level masking technique renormalization.
- It can typically update Token-Level Masking Technique Mask States dynamically as token-level masking technique generation progresses through token-level masking technique output sequences.
- It can typically enforce Token-Level Masking Technique Hard Constraints with token-level masking technique zero probability for token-level masking technique forbidden tokens.
- It can typically implement Token-Level Masking Technique Soft Constraints through token-level masking technique probability reduction rather than token-level masking technique complete elimination.
- It can often combine with Token-Level Masking Technique Beam Search for token-level masking technique multi-path exploration under token-level masking technique constraints.
- It can often optimize Token-Level Masking Technique Computation through token-level masking technique sparse representations and token-level masking technique batch processing.
- It can often handle Token-Level Masking Technique Tokenization Issues when token-level masking technique constraint boundarys split token-level masking technique subword tokens.
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- It can range from being a Static Token-Level Masking Technique to being a Dynamic Token-Level Masking Technique, depending on its token-level masking technique mask update frequency.
- It can range from being a Binary Token-Level Masking Technique to being a Weighted Token-Level Masking Technique, depending on its token-level masking technique mask values.
- It can range from being a Local Token-Level Masking Technique to being a Global Token-Level Masking Technique, depending on its token-level masking technique context scope.
- It can range from being a Sparse Token-Level Masking Technique to being a Dense Token-Level Masking Technique, depending on its token-level masking technique mask density.
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- Example(s):
- Counter-Example(s):
- See: Constraint Enforcement Technique, Token Generation Process, Probability Distribution, Language Model Output Layer, Structured Generation Framework, Constrained Decoding Algorithm, Logit Manipulation, Softmax Function, Vocabulary Space, Grammar-Guided Generation Technique, Structured Generation Framework System.