Tokenizer Tokens Tell Training Tales
Nishita Jain, Parth Upman, Shreyank N Gowda
Tokenizers are usually treated as fixed preprocessing tools, but they are trained on text and can retain traces of that text in their vocabularies. We ask whether a BPE tokenizer can reveal if a candidate dataset was part of its training corpus. The key observation is simple. Rare strings become tokens only when enough corpus frequency pushes them over the merge threshold. This suggests that membership signal should appear in rare tokens used by the candidate dataset, rather than in the order in which merges were made. We formalize this under a threshold model of BPE inclusion, showing that the likelihood-ratio test assigns its largest weights to tokens that are rare in the background but frequent in the candidate dataset. The same model suggests that merge rank and merge-graph position should not add a first-order membership signal once token existence and dataset frequency are known. We instantiate this view as RTAM-Fusion, which calibrates rare-token mass with shadow tokenizers and combines signals across rarity levels. On the tokenizer membership benchmark, RTAM-Fusion improves AUC and balanced accuracy over Vocabulary Overlap while using fewer shadow tokenizers. Existing defenses also continue to leak membership signal against adaptive attackers at production vocabulary sizes. Code: https://anonymous.4open.science/r/RTAM-Fusion-F7EE/
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