Comparing Styles across Languages: A Cross-Cultural Exploration of Politeness
Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consolidates feature importances from LMs into comparable lexical categories. We apply this framework to compare politeness, creating the first holistic multilingual politeness dataset and exploring how politeness varies across four languages. Our approach enables an effective evaluation of how distinct linguistic categories contribute to stylistic variations and provides interpretable insights into how people communicate differently around the world.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts
Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However, words are effective as communication devices because they compress ri…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LLMs' cultural sensitivities are limited t…
Multiple-choiceTANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles and Viewpoints
Hairstyles are intricate and culturally significant with various geometries, textures, and structures. Existing text or image-guided generation methods fail to handle the richness and complexity of diverse styles. We pre…
DiversityDFKI-MLT at SemEval-2026 TASK 7: Steering Multilingual Models Towards Cultural Knowledge
Large language models (LLMs) are increasingly used across diverse linguistic and cultural contexts, yet their cultural knowledge remains uneven across regions and languages. We present the DFKI-MLT system for SemEval-202…
Cross-Cultural Bias in Mel-Scale Representations: Evidence and Alternatives from Speech and Music
Modern audio systems universally employ mel-scale representations derived from 1940s Western psychoacoustic studies, potentially encoding cultural biases that create systematic performance disparities. We present a compr…
Acoustic Scene ClassificationSpeech Recognition