4 papers
OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
Yeo Jeong Park, Hyemi Jang, Minseo Choi +3
Omni-modal large language models have demonstrated remarkable potential in holistic multimodal understanding; however, the token explosion caused by high-resolution audio and video…
ARHN: Answer-Centric Relabeling of Hard Negatives with Open-Source LLMs for Dense Retrieval
Hyewon Choi, Jooyoung Choi, Hansol Jang +4
Neural retrievers are often trained on large-scale triplet data comprising a query, a positive passage, and a set of hard negatives. In practice, hard-negative mining can introduce…
Task Diversity Shortens the ICL Plateau
Jaeyeon Kim, Sehyun Kwon, Joo Young Choi +4
In-context learning (ICL) describes a language model's ability to generate outputs based on a set of input demonstrations and a subsequent query. To understand this remarkable capa…
Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model
Joo Young Choi, Jaesung R. Park, Inkyu Park +3
Current state-of-the-art diffusion models employ U-Net architectures containing convolutional and (qkv) self-attention layers. The U-Net processes images while being conditioned on…