Public opinion surveys show cross-cultural differences in political opinions between socio-cultural contexts. However, there is no clear evidence whether these differences translate to cross-lingual differences in multilingual large language models (MLLMs). We analyze whether opinions transfer between languages or whether there are separate opinions for each language in MLLMs of various sizes across five Western languages. We evaluate MLLMs’ opinions by prompting them to report their (dis)agreement with political statements from voting advice applications. To better understand the interaction between languages in the models, we evaluate them both before and after aligning them with more left or right views using direct preference optimization and English alignment data only. Our findings reveal that unaligned models show only very few significant cross-lingual differences in the political opinions they reflect. The political alignment shifts opinions almost uniformly across all five languages. We conclude that in Western language contexts, political opinions transfer between languages, demonstrating the challenges in achieving explicit socio-linguistic, cultural, and political alignment of MLLMs.

Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfairoutcomes across groups. Prior work has employed so-called personas, sociodemographic user attributes conveyed to a model, to studybias in LLMs by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. This disregards LLM sensitivity to prompt variation and the rarity of some cues in real interactions (external validity). We compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks. While cues are overall highly correlated, they produce sub-stantial variance in responses across personas that can change findings on persona-induced differences and bias. We therefore cautionagainst claims based on single persona cues, especially when they are overly explicit and have low external validity.