SMU-NLP Lab

Sookmyung Women's University Natural Language Processing Lab

SMU-NLP is the Natural Language Processing Lab at Sookmyung Women’s University. We work outward from language understanding and generation into AI agents, evaluation, resource building, human-integrated AI, and turning research results into real applications.

Our goal is to find problems nobody has properly solved yet and push the frontier of NLP research forward. We build the core techniques behind understanding and generating language, design agents that plan and act on their own, and work out evaluation methods we can actually trust. We explore how people and AI can work side by side, and carry what we learn into real applications.

contact

연구실 참여, 협업 등 문의는 아래 이메일로 연락주시기 바랍니다.

hyns.moon@sookmyung.ac.kr

news

Sep 01, 2026 숙명여자대학교 자연어처리연구실(SMU-NLP)이 문을 열었습니다. 자연어처리를 중심 주제로 하여 AI Agents, Data Evaluation, Model Evaluation, Benchmark, Resource Construction, Multilinguality 등 다양한 분야를 연구합니다. 
Sep 01, 2026 공동교신저자로 참여한 논문 3편이 EMNLP에 게재승인을 받았습니다.
  • [Main] SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges
  • [Findings] DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models
  • [Findings] SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval

selected publications

  1. EMNLP
    SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges
    Seongtae Hong, Youngjoon Jang, Jia-Heui Ju, Hyeonseok Moon, and Heuiseok Lim
    Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, Oct 2026
  2. EMNLP
    SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval
    Youngjoon Jang, Seongtae Hong, Hyeonseok Moon, and Heuiseok Lim
    Findings of the Association for Computational Linguistics: EMNLP 2026, Oct 2026
  3. EMNLP
    DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models
    Jungseob Lee, Seongtae Hong, Seungjun Lee, Jaehyung Seo, Junyoung Son, Sugyeong Eo, Chanjun Park, Hyeongju Park, Hyeonseok Moon, and Heuiseok Lim
    Findings of the Association for Computational Linguistics: EMNLP 2026, Oct 2026
  4. KBS
    SERA: Self-referential assessment framework for bidirectional generative commonsense reasoning
    Jaehyung Seo, Hyeonseok Moon, Yoonna Jang, and Heuiseok Lim
    Knowledge-Based Systems, 2026
    DOI
  5. ACL
    Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation
    Jeongho Yoon, Chanhee Park, Yongchan Chun, Hyeonseok Moon, and Heuiseok Lim
    Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2026
    DOI
  6. ACL Findings
    NeedleChain: Measuring Intact Context Comprehension Capability of Large Language Models
    Hyeonseok Moon and Heuiseok Lim
    Findings of the Association for Computational Linguistics: ACL 2026, Jul 2026
    DOI
  7. SIGIR
    Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation
    Youngjoon Jang, Seongtae Hong, Hyeonseok Moon, and Heuiseok Lim
    Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2026
    DOI
  8. ICLR
    Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
    Seongtae Hong, Youngjoon Jang, Jungseob Lee, Hyeonseok Moon, and Heuiseok Lim
    The Fourteenth International Conference on Learning Representations, 2026
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  9. EMNLP
    Metric Calculating Benchmark: Code-Verifiable Complicate Instruction Following Benchmark for Large Language Models
    Hyeonseok Moon, Seongtae Hong, Jaehyung Seo, and Heuiseok Lim
    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025
    DOI
  10. EMNLP Findings
    LimaCost: Data Valuation for Instruction Tuning of Large Language Models
    Hyeonseok Moon, Jaehyung Seo, Seonmin Koo, Jinsung Kim, Young-kyoung Ham, Jiwon Moon, and Heuiseok Lim
    Findings of the Association for Computational Linguistics: EMNLP 2025, Nov 2025
    DOI
  11. EMNLP
    The Impact of Negated Text on Hallucination with Large Language Models
    Jaehyung Seo, Hyeonseok Moon, and Heuiseok Lim
    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025
    DOI
  12. ACL
    Call for Rigor in Reporting Quality of Instruction Tuning Data
    Hyeonseok Moon, Jaehyung Seo, and Heuiseok Lim
    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Jul 2025
    DOI
  13. NAACL Findings
    Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models
    Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee, Chanjun Park, and Heuiseok Lim
    Findings of the Association for Computational Linguistics: NAACL 2025, Apr 2025
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