1 citations · 1 across the 5 of their papers we have counts for
5 papers
SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter
Lee Jung-Mok, Kim Sung-Bin, Joohyun Chang +2
Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understand…
mEOL: Training-Free Instruction-Guided Multimodal Embedder for Vector Graphics and Image Retrieval
Kyeong Seon Kim, Baek Seong-Eun, Lee Jung-Mok +1
Scalable Vector Graphics (SVGs) function both as visual images and as structured code that encode rich geometric and layout information, yet most methods rasterize them and discard…
A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search
Baek Seong-Eun, Lee Jung-Mok, Kim Sung-Bin +1
Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However, LoRA is highly sensitive to hyperpar…
Automated Model Discovery via Multi-modal & Multi-step Pipeline
Lee Jung-Mok, Nam Hyeon-Woo, Moon Ye-Bin +2
Automated model discovery is the process of automatically searching and identifying the most appropriate model for a given dataset over a large combinatorial search space. Existing…
AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models
Kim Sung-Bin, Oh Hyun-Bin, JungMok Lee +3
Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perce…