Lost in Compression: A Controlled Cross-Lingual Audit of Extractive Prompt Compressors
Extractive prompt compression promises to cut LLM inference costs by removing low-information tokens, and learned compressors such as LLMLingua-2 report strong results on English benchmarks. Most other languages already pay a token premium: the same content costs 1.3-1.8x more tokens than in English. We ask whether compression closes or widens this gap. Using fully parallel data in ten languages spanning five scripts, with controls budget-matched in the target model's tokenizer, we audit four learned compressors against four deterministic baselines, on eleven target models from ten vendors (over 250,000 evaluation calls). Three of the compressors are trained with English supervision (LLML...
| Authors | Mantas Lukauskas |
|---|---|
| Primary category | cs.CL |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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