Papers by Sanghee Park
Evaluating Multimodal Generative AI with Korean Educational Standards (2025.naacl-short)
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| Challenge: | Current benchmarks focus on English, overlooking the linguistic diversity worldwide and offering limited insights into low-resource languages like Korean. |
| Approach: | They propose to use Korean national educational tests to evaluate AI systems using a benchmark dataset. |
| Outcome: | The proposed benchmarks evaluate models in less-explored languages and open-source code and dataset builder will be fully open-sourced. |
MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge (2026.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) are increasingly used as automatic judges . however, their reliability and vulnerabilities to biases remain underexplored . |
| Approach: | They propose a benchmark to evaluate MLLMs that fail to integrate visual cues . they also introduce a test to evaluate the reliability of MLMLs based on a set of asymmetric evaluation tendencies. |
| Outcome: | Experiments on 26 state-of-the-art MLLMs reveal modality neglect and asymmetric evaluation tendencies . a standardized model with a benchmark enables a fine-grained diagnosis of nine bias types . |
Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models (2023.emnlp-main)
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Geewook Kim, Hodong Lee, Daehee Kim, Haeji Jung, Sanghee Park, Yoonsik Kim, Sangdoo Yun, Taeho Kil, Bado Lee, Seunghyun Park
| Challenge: | Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. |
| Approach: | They propose a novel neural architecture to enhance language-image understanding capability of LLMs by capturing intricate details that are often overlooked in existing methods. |
| Outcome: | The proposed model can achieve better comprehension of language information in visual contexts within images. |