1 citations · 1 across the 5 of their papers we have counts for
6 papers
KBS: A Korean Benchmark Suite for Foundation Model Evaluation
Yeonkyoung So, Jongmin Kim, Sungmok Jung +13
We present a Korean Benchmark Suite (KBS) comprising Ko-ARC, Ko-GSM8K, Ko-EQ-Bench, Ko-WinoGrande, Ko-LAMBADA, and Ko-IFEval, covering six capabilities across 9,396 items. The suit…
What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction
Jewon Yeom, Hanseul Kim, Jeongjae Park +3
Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their la…
Models Know Models Best: Evaluation via Model-Preferred Formats
Joonhak Lee, Sungmok Jung, Jongyeon Park +1
Performance of Large Language Models (LLMs) on multiple-choice tasks differs markedly between symbol-based and cloze-style evaluation formats. The observed discrepancies are system…
Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding
Sungmok Jung, Yeonkyoung So, Joonhak Lee +3
Although negation is known to challenge large language models (LLMs), benchmarks for evaluating negation understanding-especially in Korean-are scarce. We conduct a corpus-based an…
Less Is More: Reducing Token Counts Without Compromising Performance
Gyeongje Cho, Yeonkyoung So, Sangmin Lee +1
Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and generation cost. Although longer, multi…
Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding
Yeonkyoung So, Gyuseong Lee, Sungmok Jung +4
Negation is a fundamental linguistic phenomenon that poses ongoing challenges for Large Language Models (LLMs), particularly in tasks requiring deep semantic understanding. Current…