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cs.CL2025

Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs

Mario Sanz-Guerrero, Minh Duc Bui, Katharina von der Wense

When evaluating large language models (LLMs) with multiple-choice question answering (MCQA), it is common to end the prompt with the string "Answer:" to facilitate automated answer…

cs.CL20251 cited

Large Language Models Discriminate Against Speakers of German Dialects

Minh Duc Bui, Carolin Holtermann, Valentin Hofmann +2

Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Ad…

cs.CL2025

On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures

Minh Duc Bui, Kyung Eun Park, Goran Glavaš +2

Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state facts using any measurement system of thei…

cs.CL2024

Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision-Language Models

Minh Duc Bui, Katharina von der Wense, Anne Lauscher

Warning: this paper contains content that may be offensive or upsetting Hate speech moderation on global platforms poses unique challenges due to the multimodal and multilingual na…

cs.CL2024

Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget

Minh Duc Bui, Fabian David Schmidt, Goran Glavaš +1

Compared to standard language model (LM) pretraining (i.e., from scratch), Knowledge Distillation (KD) entails an additional forward pass through a teacher model that is typically…