1 citations · 1 across the 4 of their papers we have counts for
6 papers
Thunder-DeID+: Boundary-Aware Korean De-identification from Court Judgments to Broader Sensitive Documents
Gyuseong Lee, Gyeongje Cho, Jongmin Kim +8
This study examines the transferability of a de-identification model developed for Korean court judgments to a broader range of sensitive documents, focusing on boundary precision,…
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…
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…
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…