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Chaeyun Kim

3 accepted papers

2026

CAGE: A Framework for Culturally Adaptive Red-Teaming Benchmark Generation

ICLR 2026poster

Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in local culture and law, creating a critical blind spot in LLM safety evaluation. To address this gap, we introduce CAGE (Culturally Adaptive Generation), a…

Cited by 0SourceScholar
2026

Fine-Grained Multi Image Object Hallucination Benchmark

CVPR 2026

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object hallucination--generating plausible yet factually inconsistent descriptions about objects. Exi

Cited by 0SourceScholar
2024

Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation

ECCV 2024poster

"Referring Image Segmentation is a comprehensive task to segment an object referred by a textual query from an image. In nature, the level of difficulty in this task is affected by the existence of similar objects and the complexity of the referring expression. Recent RIS models still show a signifi…

Cited by 0SourcePDFScholar