Papers by Junyeong Park
Are they lovers or friends? Evaluating LLMs’ Social Reasoning in English and Korean Dialogues (2026.acl-long)
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Eunsu Kim, Junyeong Park, Juhyun Oh, Kiwoong Park, Seyoung Song, A. Seza Doğruöz, Alice Oh, Najoung Kim
| Challenge: | Existing studies on LLMs' ability to infer social relationships have limited results for Korean and English. |
| Approach: | They propose a social reasoning task based on a 1.1k-dialogue dataset in English and Korean sourced from movie scripts to evaluate LLMs' ability to infer the social relationships between speakers. |
| Outcome: | The proposed task evaluates the ability of LLMs to infer the social relationships between speakers in 1.1k-dialogue datasets in English and Korean. |
Diffusion Models Through a Global Lens: Are They Culturally Inclusive? (2025.acl-long)
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Zahra Bayramli, Ayhan Suleymanzade, Na Min An, Huzama Ahmad, Eunsu Kim, Junyeong Park, James Thorne, Alice Oh
| Challenge: | Text-to-image diffusion models have produced compelling, detailed images from text prompts, but their ability to accurately represent cultural nuances remains an open question. |
| Approach: | They propose a benchmark to evaluate whether diffusion models can generate culturally specific images spanning ten countries. |
| Outcome: | The proposed model fails to generate culturally specific images spanning ten countries . it shows significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images. |
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor (2026.acl-long)
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Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka, Seungbeen Lee, Jaeyoung Lee, Hyeju Jang, Alice Oh, Youngjae Yu
| Challenge: | Large Language Models (LLMs) absorb social and cultural biases embedded in vast web-scale corpora and are increasingly deployed in high-stakes domains such as hiring, education, and law. |
| Approach: | They propose a framework to investigate counterfactual unfairness through humor by observing how the model’s responses change when we swap who speaks and who is addressed while holding other factors constant. |
| Outcome: | The proposed framework covers humor generation refusal, speaker intention inference, and relational/societal impact prediction tasks. |
Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance (2026.eacl-long)
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| Challenge: | Existing methods for image-to-image translation lack structural integrity and attribute-specific control . Existing approaches lack semantics and provide fine-grained, attribute-based control compared to GAN-based methods . |
| Approach: | They propose a language-grounded attribute-controllable translation framework that grounds semantic differences into corresponding visual transformations while preserving unrelated structural and semantic content. |
| Outcome: | Experiments on CelebA(Dialog) and BDD100K show that LACE achieves high visual fidelity, structural preservation, and interpretable domain-specific control, surpassing baselines. |