6 papers
NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap
Dasol Choi, Joonyong Park, Daegon Yu +3
We introduce NOLLI, a procedurally generated English-Korean puzzle benchmark designed to diagnose where Korean performance gaps arise. It comprises 15 puzzle types (25 tasks; 7,500…
What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models
Dasol Choi, Guijin Son, Hanwool Lee +7
Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. U…
No Language Data Left Behind: A Comparative Study of CJK Language Datasets in the Hugging Face Ecosystem
Dasol Choi, Woomyoung Park, Youngsook Song
Recent advances in Natural Language Processing (NLP) have underscored the crucial role of high-quality datasets in building large language models (LLMs). However, while extensive r…
Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition
Dasol Choi, Seunghyun Lee, Youngsook Song
Vision-Language Models (VLMs) have shown capabilities in interpreting visual content, but their reliability in safety-critical scenarios remains insufficiently explored. We introdu…
KoGEC : Korean Grammatical Error Correction with Pre-trained Translation Models
Taeeun Kim, Semin Jeong, Youngsook Song
This research introduces KoGEC, a Korean Grammatical Error Correction system using pre\--trained translation models. We fine-tuned NLLB (No Language Left Behind) models for Korean…
Stop learning it all to mitigate visual hallucination, Focus on the hallucination target
Dokyoon Yoon, Youngsook Song, Woomyong Park
Multimodal Large Language Models (MLLMs) frequently suffer from hallucination issues, generating information about objects that are not present in input images during vision-langua…