Research

Any research topic related to natural language processing is within the interest of HYU NLP Lab. members.
Specifically, we are recently paying more attention to the topics such as:

• Effective and efficient utilization of large language models & spread of the benefits of scale into reasonable environments

  • • Parameter-efficient transfer learning

    • In-context learning and prompt engineering

    • Knowledge distillation, pruning, quantization, etc.

    • LLMs with longer context

• Guarantee for equivalent benefits of NLP to everyone

  • • Democratization (reverse-engineering) of related technologies

    • Multilingual language models & cross-lingual transfer & Korean NLP

    • Research related to bias, fairness, safety, ethics, etc.

    • Code generation

• Sustainable update of knowledge

  • • Continual learning

    • Retrieval-augmented generation

    • Models augmented with tools

    • Building domain/task-specific (customized) specialists 

    • Domain adaptation for temporal/distributional shift

    • Knowledge editing

• Text representation learning and related applications

  • • Contrastive learning

    • Dense retrieval & open-domain question answering

    • Multi-label classification

• Interpretability and explainable AI (XAI) for NLP

  • • Probing: analysis of the inner workings of existing approaches and models

    • Fusion of linguistic theories and neural approaches

    • Construction of evaluation protocols from diverse (e.g., linguistic, societal) perspectives

• Natural language generation

  • • Consistent and factual generation without hallucination

    • Detection of machine-generated text

    • Dialogue system

• Multimodality

  • • Combination of visual (image, video), textual (including multilingualism & programming languages), tabular, and vocal features

• 책임 연구

  • • 음성인식 고도화를 위한 복합의도 모델 연구, 현대자동차 (현대 NGV), 2024-2026

    • 자연어 기반 복합 의도 알고리즘 연구, 현대자동차 (현대 NGV), 2023-2024

    • 대규모 언어 모델 기반 고품질 문장 표현 학습을 위한 효율적인 방법론 분석 및 개발, 한국연구재단 (NRF), 2022-2024

    • 음성인식 자연어처리엔진의 차량 도메인 언어 이해 향상을 위한 사전학습언어모델 아키텍처 고도화, 현대자동차 (현대NGV), 2022-2023

• 공동 연구

  • • 한양대학교 인공지능대학원지원사업, 정보통신기획평가원 (IITP), 2021-

    • 한양대학교 컴퓨터소프트웨어학과 BK21-FOUR 인공지능 혁신인재 교육단, 한국연구재단 (NRF), 2022-

    • 초연결사회 위험 관리를 위한 빅데이터 기반 사회 환경 실시간 모니터링/사회 시뮬레이션 시스템, 한국연구재단 (NRF), 2022-

    • 인공지능반도체대학원(한양대학교), 정보통신기획평가원 (IITP), 2023-

• Visit here!

(24/02/20) One paper (BlendX: Complex Multi-Intent Detection with Blended Patterns) has been accepted for presentation at LREC-COLING 2024. Big congrats to Yejin, Jungyeon, and Kangsan!

(24/02/15) Jinhyeon, Young Hyun, Taejun, and Seong Hoon have graduated with their Master's degrees. Wish them all their best!

(23/11/22) One paper has been accepted to KSC 2023. Congrats to Jii!

(23/10/08) Two papers (X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity & Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP) have been accepted at EMNLP 2023 (Findings).  X-SNS is especially our first internal project that will be showcased at a major conference.  Congrats to Taejun, Jinhyeon, Deokyoung, and Seong Hoon. See you in Singapore!
(23/09/25) Two papers have been accepted to HCLT 2023. Congrats to Jinhyeon and Taejun!

(23/06/30) Updated the contents regarding our journey to KCC 2023, which took place on the Jeju island. At the conference, Seong Hoon presented his work titled Enhanced Zero-Shot Cross-Lingual Transfer with the Diversification of Source Languages.

(22/11/25) One paper (Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners) has been accepted to AAAI 2023. Congrats!

(22/10/16) Taeuk, Kangsan, Jinhyeon, Minjin, and Kyumin participated in COLING 2022.

(22/10/07) Two papers (Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations and Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble) for which Taeuk participated as a corresponding author have been accepted to EMNLP 2022 (1 Main, 1 Findings). They are the results of a collaboration with SNU and Naver. See you in Abu Dhabi!

(22/10/04) One paper (Comparison and Analysis of Unsupervised Contrastive Learning Approaches for Korean Sentence Representations) has been accepted to HCLT 2022 (authors: Young Hyun, Kyumin, Minjin, Jii, Kangsan, and Taeuk). Congrats!

(22/08/17) Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models (authors: Taeuk) has been accepted to COLING 2022. See you soon in Gyeongju!

(22/05/22) Taeuk has been invited to serve as an Area Chair for the Unsupervised and Weakly-supervised Methods Track of EMNLP 2022.

(22/04/13) HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization (authors: Youngju and Taeuk) has been accepted to SemEval 2022. Congrats!

(21/10/15) Taeuk has been nominated to serve as an Action Editor for the ACL Rolling Review (a new reviewing platform for *ACL conferences)!

(21/08/26) One paper (Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models) is accepted at EMNLP 2021 Findings.

(21/08/03) Taeuk presented his work Self-Guided Contrastive Learning for BERT Sentence Representations at ACL 2021.