5 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…
Ko-MuSR: A Multistep Soft Reasoning Benchmark for LLMs Capable of Understanding Korean
Chanwoo Park, Suyoung Park, JiA Kang +6
We present Ko-MuSR, the first benchmark to comprehensively evaluate multistep, soft reasoning in long Korean narratives while minimizing data contamination. Built following MuSR, K…
Beyond Line-Level Filtering for the Pretraining Corpora of LLMs
Chanwoo Park, Suyoung Park, Yelim Ahn +3
While traditional line-level filtering techniques, such as line-level deduplication and trailing-punctuation filters, are commonly used, these basic methods can sometimes discard v…
UDC-VIT: A Real-World Video Dataset for Under-Display Cameras
Kyusu Ahn, JiSoo Kim, Sangik Lee +4
Even though an Under-Display Camera (UDC) is an advanced imaging system, the display panel significantly degrades captured images or videos, introducing low transmittance, blur, no…
Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration
Kyusu Ahn, Jinpyo Kim, Chanwoo Park +2
Under-Display Camera (UDC) houses a digital camera lens under a display panel. However, UDC introduces complex degradations such as noise, blur, decrease in transmittance, and flar…