Papers by Inho Won
TReX: Tokenizer Regression for Optimal Data Mixture (2026.eacl-long)
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| Challenge: | Existing approaches to train and inference tokenizers rely on heuristics or large-scale searches to determine optimal data mixtures. |
| Approach: | They propose a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training. |
| Outcome: | The proposed model outperforms mixtures based on LLaMA3 and uniform distributions by up to 12% in both in- and out-of-distribution compression efficiency. |
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment (2024.findings-naacl)
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DongJae Shin, HyeonSeok Lim, Inho Won, ChangSu Choi, Minjun Kim, SeungWoo Song, HanGyeol Yoo, SangMin Kim, KyungTae Lim
| Challenge: | constructing multilingual data for large multimodal models presents its own set of challenges due to language diversity and complexity. |
| Approach: | They propose to use GPT4-V to construct multimodal training datasets using a text-only version of GPT4. |
| Outcome: | The proposed method performs well in Korean and English, surpassing existing methods. |
TELLME: Test-Enhanced Learning for Language Model Enrichment (2026.findings-eacl)
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Minjun Kim, Inho Won, HyeonSeok Lim, MinKyu Kim, Junghun Yuk, Wooyoung Go, Jongyoul Park, Jungyeul Park, KyungTae Lim
| Challenge: | Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models, but has faced challenges such as acquiring large-scale domain-specific datasets and high computational costs. |
| Approach: | They propose a method that integrates the Test-Enhanced Learning principle with CPT to promote efficient domain-specific knowledge acquisition and long-term memory retention. |
| Outcome: | The proposed method outperforms existing methods by 23.6% in the financial domain and achieves 9.8% improvement in long-term memory retention. |
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation (2025.coling-main)
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Hyeonseok Lim, Dongjae Shin, Seohyun Song, Inho Won, Minjun Kim, Junghun Yuk, Haneol Jang, KyungTae Lim
| Challenge: | Existing evaluation datasets for external knowledge-based VQA lack a capability to determine which passage is useful for answering queries. |
| Approach: | They propose a visual question answering benchmark for vision language models based on retrieval augmented generation (RAG) the proposed benchmark includes five input passages, a capability lacking in previous research. |
| Outcome: | The proposed benchmark includes five input passages and is validated using the state-of-the-art Llama3-based VLM, the Llava-Llamama-3 model. |
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean (2024.lrec-main)
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ChangSu Choi, Yongbin Jeong, Seoyoon Park, Inho Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
| Challenge: | Large language models (LLMs) use pretraining to predict the subsequent word, but less-resourced languages are being overlooked. |
| Approach: | They propose to expand the MLLM vocabularies to enhance expressiveness and use bilingual data for pretraining to align the high- and less-resourced languages. |
| Outcome: | The proposed model outperforms existing models in qualitative analyses compared to Korean monolingual models. |
ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs (2026.eacl-industry)
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Hangyeol Yoo, ChangSu Choi, Minjun Kim, Seohyun Song, SeungWoo Song, Inho Won, Jongyoul Park, Cheoneum Park, KyungTae Lim
| Challenge: | Recent studies have focused on enhancing multilingual large language models (MLLMs) for specific languages. |
| Approach: | They propose an efficient layer-specific optimization method to enhance continual pretraining (CP) for specific languages in multilingual large language models (MLLMs). |
| Outcome: | The proposed method achieves a training speedup of up to 6.46 times compared to existing methods while improving target language performance by up to 5.2% on qualitative benchmarks. |