2 citations · 3 across the 4 of their papers we have counts for
6 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…
TELLME: Test-Enhanced Learning for Language Model Enrichment
Minjun Kim, Inho Won, Hyeonseok Lim +6
Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models. However, CPT has consistently been accompanied by challenges, such…
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…
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…