Papers by Junghun Park
Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification (2026.acl-long)
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| Challenge: | Existing large language model-based readability control methods rely on pre-labeled sentence corpora and primarily target English. |
| Approach: | They propose a framework for adaptive multilingual text simplification without parallel corpora supervision that integrates three reward modules: vocabulary coverage, semantic preservation, and coherence. |
| Outcome: | The proposed framework achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency compared to stronger LLMs. |
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. |
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 . |
Unified Automated Essay Scoring and Grammatical Error Correction (2025.findings-naacl)
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| Challenge: | a new study explores the integration of automated writing evaluation and grammatical error correction through multitask learning. |
| Approach: | They propose a system that integrates automated writing evaluation and grammatical error correction through multitask learning by leveraging a shared learning framework. |
| Outcome: | The proposed system outperforms models trained on AWE and GEC, the authors show . their study demonstrates that the proposed system improves writing assessment accuracy and accuracy . |