4 citations · 4 across the 5 of their papers we have counts for
6 papers
The Role of Fine-grained Harm Signals in LLM Safety
Soyeon Park, Seogyeong Jeong, Sunwoo Kim +1
Prior work has shown that internal harmfulness representations in large language models vary across risk categories, while sharing a common general harm representation component. T…
LoCar: Localization-Aware Evaluation of In-Vehicle Assistants through Fine-Grained Sociolinguistic Control
Seogyeong Jeong, Kiwoong Park, Seyoung Song +4
While Large Language Models (LLMs) are increasingly integrated into in-vehicle conversational systems, identifying the optimal model remains challenging due to the lack of domain-s…
CulTrace: Tracing Internal Cultural Reasoning in Large Language Models
Haeun Yu, Arnav Arora, Seogyeong Jeong +7
The growing deployment of large language models (LLMs) across diverse cultural contexts necessitates a deeper understanding of models' hidden representations of different cultures.…
MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language
Seyoung Song, Seogyeong Jeong, Eunsu Kim +4
Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We p…
LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation
Junyeong Park, Seogyeong Jeong, Seyoung Song +2
Content moderation is a global challenge, yet major tech platforms prioritize high-resource languages, leaving low-resource languages with scarce native moderators. Since effective…
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Paul Röttger, Giuseppe Attanasio, Felix Friedrich +19
Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards,…