5 citations · 7 across the 7 of their papers we have counts for
10 papers
Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP
Minjun Kim, Hyunmin Song, Changwon Lim
Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Although prior work has introd…
Strong Helps Weak: Directional Cross-Modal Alignment Transfer in Multi-modal LLMs
Hoigi Seo, Byung Hyun Lee, Minjun Kim +3
Multi-modal large language models (MLLMs) achieve strong modality understanding by pairing a large language model (LLM) with an encoder for a target modality such as vision, video,…
Distribution-aware Language Neuron Identification in Multilingual Large Language Models
Minjun Kim, Inho Won, Junghun Yuk +3
Multilingual large language models (mLLMs) contain a small fraction of feed-forward neurons that are sensitive to particular languages, commonly termed language-specific neurons. E…
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…
CRIT: Graph-Based Automatic Data Synthesis to Enhance Cross-Modal Multi-Hop Reasoning
Junyoung Sung, Seungwoo Lyu, Minjun Kim +3
Real-world reasoning often requires combining information across modalities, connecting textual context with visual cues in a multi-hop process. Yet, most multimodal benchmarks fai…
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…
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…
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining
Minjun Kim, Seungwoo Song, Youhan Lee +2
The current research direction in generative models, such as the recently developed GPT4, aims to find relevant knowledge information for multimodal and multilingual inputs to prov…