6 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…
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…
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…
Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs
Kyubyung Chae, Gihoon Kim, Gyuseong Lee +3
Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governme…
Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments
Sungeun Hahm, Heejin Kim, Gyuseong Lee +2
To ensure a balance between open access to justice and personal data protection, the South Korean judiciary mandates the de-identification of court judgments before they can be pub…