Translation Equivalence Is Not Linguistic Equity: English–Arabic–Chinese Calibration and Cultural-Item Performance Gaps in a Compact Multilingual Language Model
DOI:
https://doi.org/10.61424/4jxy8974Keywords:
Multilingual language models, calibration, cultural sensitivity, English, Arabic, Chinese, retrieval-augmented in-context learning, selective predictionAbstract
Parallel translation simplifies multilingual evaluation, but it does not guarantee equivalent model behavior. This study evaluated a quantized 135-million-parameter multilingual language model on the English, Arabic, and Chinese test slices of Global-MMLU-Lite. Six complete native and retrieval-augmented runs produced 2,400 item-level predictions. Cross-language inference used 390 genuinely aligned items, including equal numbers of culturally sensitive and culturally agnostic questions; a 387-item strict cohort excluded three questions with duplicated answer options. Native accuracy was 26.7% in English, 26.9% in Arabic, and 25.6% in Chinese, yet native-condition prediction agreement ranged from 3.3% to 52.8%, with Cohen’s κ between −.058 and .015. Same-language retrieval changed accuracy by −1.0, −0.5, and +2.8 percentage points, respectively; none of the paired effects survived Holm correction. Culturally sensitive items were directionally harder in every native-language condition, although all bootstrap intervals included zero. Calibration differed more sharply than accuracy: native expected calibration error was .102 in English, .069 in Arabic, and .308 in Chinese. Cross-fitted temperature scaling reduced these values mainly by flattening probabilities toward the four-choice uniform distribution, while selective risk remained high. Native Arabic and Chinese prompts required 3.04 and 2.12 times as many tokens as English prompts. The results show that score parity can coexist with different decisions, confidence failures, and token burdens. Translation equivalence should therefore be evaluated jointly through accuracy, item-level agreement, calibration, selective risk, and token burden.
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