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Faithful or evasive? An empirical study on translation norm preferences of Chinese and American LLMs in Chinese official political and policy discourse
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DOI：10.3389/frai.2026.1869972.png)
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Abstract
En 中文
IntroductionLarge language models now handle a share of cross-lingual political translation; but no study has directly measured whether they follow stable normative preferences when doing so.MethodsWe target Chinese-to-English translation of Chinese political and policy terms embedded in authentic official discourse. Mapping 52 publications across translation theory; LLM empirics; and AI alignment yields a five-dimensional Translation Norm Orientations (TNO) framework: Faithfulness (FN); Fluency (FLN); Cultural Adaptation (CAN); Ethical Hedging (EHN); and Foreignization Retention (FRN). Eight mainstream Chinese and American LLMs complete a Forced-Choice Translation Ranking Task (FCTRT) on 20 ideologically sensitive political terms; ranked across three repeated trials. Kendall’s W assesses ranking stability; Mann–Whitney U tests with BH-FDR correction test between-group differences; and hierarchical clustering identifies latent normative prototypes.ResultsAll eight models rank FN first; CAN; EHN; and FRN settle into a shared low-priority band beneath it. None of the five dimensions shows a statistically significant CN–US split. FN alone carries a medium effect size (Cliff’s δ = 0.50); which points to a difference in how strongly; not which way; the two groups lean. Cluster membership cuts across national lines rather than following it: four transnational profiles emerge; separated by foreignization tolerance rather than developer geography.DiscussionThese results tie translation norm theory to AI evaluation and supply a benchmark other researchers can reuse to examine LLM-mediated political communication.
Keywords:
large language models
cross-cultural comparison
Chinese official political and policy discourse
forced-choice translation ranking task
translation norm orientations
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