Honorifics encode social hierarchies and relational nuances, making their correct use a culturally sensitive yet challenging aspect of translation. In doing so, they reflect and shape how individuals position themselves and others within a social world. In this work, we investigate how different translation models handle Korean honorifics, both in implicit scenarios, where only the sentence is given, and explicit scenarios. Our findings are as follows: (i) large language models (LLMs) fine-tuned for translation (MTLMs) consistently prefer polite forms more than their instruction-tuned counterparts in both scenarios; (ii) sequence-to-sequence models produce less polite outputs in implicit contexts but shift toward more polite forms when the addressee is explicitly provided; and (iii) both types of LM-based models tend to become more casual when the addressee is known. When compared with human preferences, MTLMs diverge more strongly, exhibiting a systematic overuse of polite forms relative to human judgments.

Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state using any measurement system of their choice. Being available to users from diverse cultural backgrounds, Large Language Models (LLMs) should also be able to provide accurate information irrespective of the measurement system at hand. Using newly compiled datasets we test if this is truly the case for seven open-source LLMs, addressing three key research questions: (RQ1) What is the default system used by LLMs for each type of measurement? (RQ2) Do LLMs’ answers and their accuracy vary across different measurement systems? (RQ3) Can LLMs mitigate potential challenges w.r.t. underrepresented systems via reasoning? Our findings show that LLMs default to the measurement system predominantly used in the data. Additionally, we observe considerable instability and variance in performance across different measurement systems. While this instability can in part be mitigated by employing reasoning methods such as chain-of-thought (CoT), this implies longer responses and thereby significantly increases test-time compute (and inference costs), marginalizing users from cultural backgrounds that use underrepresented measurement systems.