4 papers
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-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…
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…