3 papers
cs.CL2026
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…
cs.CL2025
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…
cs.CL2025
Choices Speak Louder than Questions
Gyeongje Cho, Yeonkyoung So, Jaejin Lee
Recent findings raise concerns about whether the evaluation of Multiple-Choice Question Answering (MCQA) accurately reflects the comprehension abilities of large language models. T…