@inproceedings{zhang-etal-2026-released,
title = "Can Released {LLM} Vocabularies Support Token-Level Estimation of Hidden Corpora?",
author = "Zhang, Qingjie and
Ren, Xingzhang and
Chen, Zixuan and
Li, Jinfeng and
Chen, YueFeng and
Yang, Yitong and
Xue, Hui and
Liu, Dayiheng and
Qiu, Han",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1021/",
pages = "21582--21596",
ISBN = "979-8-89176-462-0",
abstract = "Pretraining corpus composition shapes LLM capabilities, but it often remains hidden even when model weights are released. Prior work has inferred corpus mixtures or traced specific token groups from released tokenizer vocabularies; in contrast, we estimate corpus ratios for arbitrary target tokens. We first show that BPE tokenizers trained on different corpora share stable token ID{--}ratio distributions, motivating distribution transfer from known corpora to a target tokenizer trained on hidden corpora. We then propose Quantile-Guided Density Estimation (QGDE), which approximates this distribution with multiple quantile trends and uses local density weighting to produce token-level estimates. In controlled settings and a realistic setting using the released SmolLM tokenizer, QGDE achieves mean relative errors as low as 3.00{\%} for token-level estimation and 3.08{\%} after aggregation into category-level mixtures. These results suggest that released tokenizer vocabularies provide a useful signal for fine-grained corpus estimation beyond coarse composition inference."
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<abstract>Pretraining corpus composition shapes LLM capabilities, but it often remains hidden even when model weights are released. Prior work has inferred corpus mixtures or traced specific token groups from released tokenizer vocabularies; in contrast, we estimate corpus ratios for arbitrary target tokens. We first show that BPE tokenizers trained on different corpora share stable token ID–ratio distributions, motivating distribution transfer from known corpora to a target tokenizer trained on hidden corpora. We then propose Quantile-Guided Density Estimation (QGDE), which approximates this distribution with multiple quantile trends and uses local density weighting to produce token-level estimates. In controlled settings and a realistic setting using the released SmolLM tokenizer, QGDE achieves mean relative errors as low as 3.00% for token-level estimation and 3.08% after aggregation into category-level mixtures. These results suggest that released tokenizer vocabularies provide a useful signal for fine-grained corpus estimation beyond coarse composition inference.</abstract>
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%0 Conference Proceedings
%T Can Released LLM Vocabularies Support Token-Level Estimation of Hidden Corpora?
%A Zhang, Qingjie
%A Ren, Xingzhang
%A Chen, Zixuan
%A Li, Jinfeng
%A Chen, YueFeng
%A Yang, Yitong
%A Xue, Hui
%A Liu, Dayiheng
%A Qiu, Han
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-released
%X Pretraining corpus composition shapes LLM capabilities, but it often remains hidden even when model weights are released. Prior work has inferred corpus mixtures or traced specific token groups from released tokenizer vocabularies; in contrast, we estimate corpus ratios for arbitrary target tokens. We first show that BPE tokenizers trained on different corpora share stable token ID–ratio distributions, motivating distribution transfer from known corpora to a target tokenizer trained on hidden corpora. We then propose Quantile-Guided Density Estimation (QGDE), which approximates this distribution with multiple quantile trends and uses local density weighting to produce token-level estimates. In controlled settings and a realistic setting using the released SmolLM tokenizer, QGDE achieves mean relative errors as low as 3.00% for token-level estimation and 3.08% after aggregation into category-level mixtures. These results suggest that released tokenizer vocabularies provide a useful signal for fine-grained corpus estimation beyond coarse composition inference.
%U https://aclanthology.org/2026.findings-emnlp.1021/
%P 21582-21596
Markdown (Informal)
[Can Released LLM Vocabularies Support Token-Level Estimation of Hidden Corpora?](https://aclanthology.org/2026.findings-emnlp.1021/) (Zhang et al., Findings 2026)
ACL
- Qingjie Zhang, Xingzhang Ren, Zixuan Chen, Jinfeng Li, YueFeng Chen, Yitong Yang, Hui Xue, Dayiheng Liu, and Han Qiu. 2026. Can Released LLM Vocabularies Support Token-Level Estimation of Hidden Corpora?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21582–21596, Budapest, Hungary. Association for Computational Linguistics.