/ Paper arXiv 2611.13441
In short
In shortReport a mistake
Key numbers
From the paperCopied from the abstract, not written by AI.
Abstract
From the paperThe authors' own words.
Most language models deployed outside the United States and China are fine-tuned descendants of a small number of pretrained families. We argue that fine-tuning leaves the decisive properties of a model inherited from its base: vocabulary, knowledge, cultural alignment, memorized data and license. We contribute (i) an updated, reproducible measurement of the tokenization premium across 31 languages and 7 tokenizers released in 2023-2024, on 27 articles of the Universal Declaration of Human Rights, with explicit correction for unknown-token collapse. Premiums reach 10x for Amharic in older tokenizers and remain at 6.8x in the newest commercial one; Indigenous South American languages pay between 1.6x and 2.7x under global tokenizers. A vocabulary built from Portuguese lowers the premium for 11 of 13 Latin-script Global South languages relative to every global tokenizer tested, by about 24% for Guarani and Quechua. (ii) An eight-test black-box sovereignty audit that a buyer can run without access to weights. (iii) A regime argument: under energy and liability constraints, accuracy per joule, calibration and selective risk should replace raw accuracy as design objectives, with metrics that abstention cannot inflate. We close with preliminary internal measurements from a proprietary architecture built around these objectives, reported with their limitations. Data and measurement code are included.
From the full paper
From the paperCopied from the paper itself by our code.
Reading the paper on arXiv…
Where it fits
The most cited papers it builds on, and the papers that build on it.
/ Builds on 3 most cited
- 201711.2k citedOn Calibration of Modern Neural Networks ↗
- 20203.6k citedExtracting Training Data from Large Language Models ↗
- 20232.5k citedNot What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection ↗
/ Built on by None yet
No papers cite it yet. We check again every week.
From Semantic Scholar, updated weekly.
Ask this paper
/ Ask a questionAnswers come from the full paper and name the section
/ Your notes
Select text in the abstract to highlight it or add a note. They stay in this browser.
