2 citations · 3 across the 11 of their papers we have counts for
13 papers
Which Language Should a Skeleton Speak? Language Choices in Multilingual Reasoning
HyeonSeok Lim, SeungWoo Song, Inho Won +3
Skeleton-based reasoning prompting is a promising training-free approach for structuring LLM reasoning, but prior work largely assumes an English-centric setting. We propose the La…
MELON: A Large-Scale Dataset for Multi-Event Text-to-Long-Video Retrieval
Chan Hur, SeungWoo Song, Jeong-hun Hong +3
Existing text-video retrieval datasets primarily consist of short-form clips containing a single dominant event. While suitable for measuring basic vision-language alignment, they…
Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
Minkyung Cho, Jihyo Kim, SeungWoo Song +4
Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that m…
Solar Open 2 Technical Report
Sungrae Park, Sanghoon Kim, Gyoungjin Gim +50
We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B). To hold entire agent tra…
Semantic Hardness Is Not Visual Hardness: Sign-Aware Hard Negative Mining for Sign Language Retrieval
Junmyeong Lee, Chan Hur, ChangSu Choi +5
Sign Language Retrieval (SLRet) enables efficient access to sign language content but remains fragile in fine-grained scenarios where visually similar signs must be distinguished.…
MentalBench: A DSM-Grounded Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models
Hoyun Song, Migyeong Kang, Jisu Shin +7
Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However, existing mental health benchmark…
ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs
HanGyeol Yoo, ChangSu Choi, Minjun Kim +6
We propose an efficient layer-specific optimization (ELO) method designed to enhance continual pretraining (CP) for specific languages in multilingual large language models (MLLMs)…
KORMo: Korean Open Reasoning Model for Everyone
Minjun Kim, Hyeonseok Lim, Hangyeol Yoo +10
This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained…
MENTOR: Reinforcement Learning via Flexible Teacher-Optimized Rewards for Tool-Use Distillation
ChangSu Choi, Hoyun Song, Dongyeon Kim +6
Distilling the tool-use capabilities of large language models (LLMs) into small language models (SLMs) is essential for their practical application. The predominant approach, super…
ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts
Dongwon Noh, Donghyeok Koh, Junghun Yuk +4
Prior benchmarks for evaluating the domain-specific knowledge of large language models (LLMs) lack the scalability to handle complex academic tasks. To address this, we introduce \…
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation
Hyeonseok Lim, Dongjae Shin, Seohyun Song +5
We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision language models (VLMs) based on retrieval augmented generation (RAG). Unlike existing ev…
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment
Dongjae Shin, Hyeonseok Lim, Inho Won +6
The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text. Howe…
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean
ChangSu Choi, Yongbin Jeong, Seoyoon Park +11
Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and rese…