Papers by Cheoneum Park
PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding (2026.findings-acl)
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| Challenge: | Masked diffusion language models (MDLMs) are limited by a monotonic unmasking policy, where committed tokens cannot be revised. |
| Approach: | They propose a training-free inference algorithm for two-phase decoding that unlocks unstable regions through deterministic window masking and stochastic leftward relaxation. |
| Outcome: | The proposed algorithm significantly improves accuracy on reasoning benchmarks on GSM8K. |
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs (2024.findings-emnlp)
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| Challenge: | Existing approaches to augmented generation of retrieved passages rely on the quality of a question's retrieved information. |
| Approach: | They propose a simple yet efficient method called question and passage augmentation via LLMs for open-domain QA. |
| Outcome: | The proposed method outperforms the state-of-the-art and achieves significant performance gain over existing methods. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts (2025.findings-emnlp)
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| Challenge: | ScholarBench evaluates domain-specific knowledge of large language models (LLMs) prior benchmarks lack the scalability to handle complex academic tasks. |
| Approach: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark is constructed through a three-step process . |
| Outcome: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark comprises 5,031 examples in Korean and 5,309 examples in English . |
Fast End-to-end Coreference Resolution for Korean (2020.findings-emnlp)
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| Challenge: | Recent advances in coreference resolution have come at a cost of computational complexity and have not been addressed. |
| Approach: | They propose a pointer network that leverages the linguistic property of head-final languages to reduce coreference linking search space and achieve 2x speedup in document processing time. |
| Outcome: | The proposed model maintains state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time. |
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. |
SCV: Light and Effective Multi-Vector Retrieval with Sequence Compressive Vectors (2025.coling-industry)
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| Challenge: | Recent advances in language models (LMs) have driven progress in information retrieval (IR), effectively extracting semantically relevant information. |
| Approach: | They propose a multi-vector retrieval framework with sequence compression vectors and a coarse-to-fine vector search framework that uses span compressive vectors to score documents. |
| Outcome: | The proposed approach achieves the fastest latency compared to other state-of-the-art models and can obtain competitive performance on both in-domain and out-of domain benchmark datasets. |