AdaBPE
An improved BPE tokenization method that optimizes tokenization of mixed corpora through dynamic frequency thresholds (length-adaptive and type-adaptive), allowing multi-word expressions to be merged across spaces and improving the encoding efficiency of formal languages.
Timeline (last 90 days)
The paper points out that existing superword tokenization methods still rely on greedy frequency merging, considering neither token length nor low-frequency but critical domain-specific language (DSL) symbols.
AdaBPE introduces a first adaptive mechanism: the longer a candidate token, the lower its frequency threshold, offsetting BPE's preference for short patterns.
AdaBPE introduces a second adaptive mechanism: it distinguishes general natural-language patterns from formal-language patterns (with LaTeX as an example) and applies a looser frequency threshold to the latter.
AdaBPE describes its own approach as "adaptive frequent pattern mining," which is essentially a dynamic filter placed before BPE's merge loop: it first adjusts the admission threshold according to the length and category of candidate patterns, then performs merging.
AdaBPE was evaluated on five datasets, covering pure LaTeX, pure natural language, and scenarios mixing the two.
Experimental results show that AdaBPE achieves a stable improvement in encoding efficiency on domain-specific content, with the same text compressed into fewer tokens.
Experimental results show that downstream language models tokenized with AdaBPE outperform the baseline on LaTeX-related tasks, while general natural language performance shows no degradation.
AdaBPE currently uses LaTeX only as a case of formal language, and the degree of automation in type classification and the specific form of the threshold function still await further validation for transfer to other DSLs.
All Facts (8)
The paper points out that existing superword tokenization methods still rely on greedy frequency merging, considering neither token length nor low-frequency but critical domain-specific language (DSL) symbols.
50%UnverifiedAdaBPE introduces a first adaptive mechanism: the longer a candidate token, the lower its frequency threshold, offsetting BPE's preference for short patterns.
50%UnverifiedAdaBPE introduces a second adaptive mechanism: it distinguishes general natural-language patterns from formal-language patterns (with LaTeX as an example) and applies a looser frequency threshold to the latter.
50%UnverifiedAdaBPE describes its own approach as "adaptive frequent pattern mining," which is essentially a dynamic filter placed before BPE's merge loop: it first adjusts the admission threshold according to the length and category of candidate patterns, then performs merging.
50%UnverifiedAdaBPE was evaluated on five datasets, covering pure LaTeX, pure natural language, and scenarios mixing the two.
50%UnverifiedExperimental results show that AdaBPE achieves a stable improvement in encoding efficiency on domain-specific content, with the same text compressed into fewer tokens.
50%UnverifiedExperimental results show that downstream language models tokenized with AdaBPE outperform the baseline on LaTeX-related tasks, while general natural language performance shows no degradation.
50%UnverifiedAdaBPE currently uses LaTeX only as a case of formal language, and the degree of automation in type classification and the specific form of the threshold function still await further validation for transfer to other DSLs.
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