Removable and Irreducible: A Token-Cost Ledger for the Multilingual Tokenization Tax
Large language models pay a well-documented tax on non-English text: the same content costs several times more tokens, and because attention is quadratic in sequence length, far more compute. We ask how much of this tax is removable. Framing the token layer as source coding -- transformer compute is monotone in sequence length, whose per-atom floor is the Shannon rate $H/\log_2 V$, an object already applied to tokenizers in prior work -- we assemble a token-cost ledger that splits each language's cost, at fixed parallel content, into a removable coding redundancy, a residual coding slack, an intrinsic-content term, and an orthogonal, irreducible grapheme-to-phoneme term that governs the m...
| Authors | Madhulatha Mandarapu, Sandeep Kunkunuru |
|---|---|
| Primary category | cs.CL |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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