4 citations · 7 across the 13 of their papers we have counts for
15 papers
A Representation-Learning Item Response Model for Identifying Behaviorally Important Actions in PIAAC Process Data
Junyeong Park, Daeun Hwangbo, Seyoung Park +2
Problem-solving log process data from computer-based assessments provide detailed information about how respondents approach and complete tasks. However, the resulting action seque…
EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers
Junyeong Park, Jieun Han, Haneul Yoo +3
Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful or inappropriate content appea…
Investigating Social Bias in Narrative Image Generation
Junyeong Park, Sowon Min, Euna Jang +5
Text-to-image (T2I) generation models are increasingly embedded in applications such as media content creation and education, raising concerns about how their outputs may reproduce…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors
Jiho Jin, Junho Myung, Juhyun Oh +5
As large language models (LLMs) are increasingly deployed to users around the world, they are integrated into everyday tasks across diverse cultural contexts, from drafting persona…
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor
Shubin Kim, Yejin Son, Junyeong Park +6
Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with humor, their reactions expose t…
Analyzing Process Data from Computer-Based Assessments: A Tutorial on Preprocessing, Feature Extraction, and Model-Based Inference
Daeun Hwangbo, Junyeong Park, Minjeong Jeon +1
Computer-based assessments routinely generate detailed interaction logs -- commonly referred to as process data -- that record every action a respondent performs during task comple…
When Language Learners Code-Switch: Korean-English Code-Switching in LLM-based Speaking Practice
Junyeong Park, Jieun Han, Yeon Su Park +5
Code-switching (CSW), the use of multiple languages within the same interaction, has been widely studied in NLP for multilingual communication. While CSW also occurs in language-le…
World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models
Eunsu Kim, Junyeong Park, Na Min An +9
In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large…
One-Topic-Doesn't-Fit-All: Transcreating Reading Comprehension Test for Personalized Learning
Jieun Han, Daniel Lee, Haneul Yoo +5
Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose…
Are they lovers or friends? Evaluating LLMs' Social Reasoning in English and Korean Dialogues
Eunsu Kim, Junyeong Park, Juhyun Oh +5
As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore their capabilities, we introduce SC…
When Tom Eats Kimchi: Evaluating Cultural Bias of Multimodal Large Language Models in Cultural Mixture Contexts
Jun Seong Kim, Kyaw Ye Thu, Javad Ismayilzada +6
In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should…
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…
Diffusion Models Through a Global Lens: Are They Culturally Inclusive?
Zahra Bayramli, Ayhan Suleymanzade, Na Min An +5
Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent vario…
Survey of Cultural Awareness in Language Models: Text and Beyond
Siddhesh Pawar, Junyeong Park, Jiho Jin +7
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ens…