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Daeyoung Kim

I am a Research Engineer at NCSoft. I received my M.S from KAIST Graduate school of AI, where I was advised by Edward Choi.

RESEARCH INTERESTS

  • Multi-modality in AI
  • Reliability of Machine Learning
  • Machine Learning for Healthcare

EXPERIENCE

  • 2023-02 – Present

    NCSoft

    Large Multimodal Model

    Alignment Tuning (RLHF, RLAIF)

    LLM-as-an-evaluator

    Writing Assistant using LLM

  • 2022-07 – 2023-01

    NAVER CLOVA, Research Engineer Intern

    Sentence Embedding using Large Language Model

    Zero-shot Text Classification

  • 2020-07 – 2020-08

    NAVER Corp, Research Engineer Intern

    Sentimental Analysis of Short Sentence

    Data Augmentation

  • 2020-01 – 2020-12

    Data Mining and Information Systems Lab, Undergraduate Researcher

    Zero-shot Text Summarization

EDUCATION

  • 2021-03 – 2023-02

    Korea Advanced Institute of Science and Technology

    Master of Artificial Intelligence (Graduate School of AI)

    Advisor Prof. Edward Choi

  • 2015-03 – 2021-02

    Korea University

    Bachelor of Department of Computer Science and Engineering

    Bachelor of Integrated Major in Information Security Convergence

PUBLICATIONS

  1. OffsetBias: Leveraging Debiased Data for Tuning Evaluators
    Junsoo Park*, Seungyeon Jwa*, Meiying Ren,  Daeyoung Kim, and Sanghyuk Choi
    In Findings in Empirical Methods in Natural Language Processing, (EMNLP), 2024
    [Paper]  [GitHub]
  2. Towards the Practical Utility of Federated Learning in the Medical Domain
    Seongjun Yang, Hyeonji Hwang,  Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, and Edward Choi
    In Proc. of Conference on Health, Inference, and Learning, (CHIL), 2023
    [Paper]  [GitHub]
  3. Revisiting the Importance of Amplifying Bias for Debiasing
    Jungsoo Lee*, Jeonghoon Park*,  Daeyoung Kim*, Juyoung Lee, Edward Choi, and Jaegul Choo
    In Proc. of Association for the Advancement of Artificial Intelligence, (AAAI), 2023, Accepted as Oral Presentation
    [Paper]  [GitHub]
  4. Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
    Daeyoung Kim, Seongsu Bae, Seungho Kim, and Edward Choi
    In Proc. of Conference on Health, Inference, and Learning, (CHIL), 2022
    [Paper]  [GitHub]
  5. Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
    Seongsu Bae,  Daeyoung Kim, Jiho Kim, and Edward Choi
    In Proc. of Machine Learning for Health, (ML4H), 2021, Accepted as Oral Presentation
    [Paper]
  6. TeSS: Zero-Shot Classification via Textual Similarity Comparison with Prompting using Sentence Encoder
    Jimin Hong*, Jungsoo Park*,  Daeyoung Kim*, Seongjae Choi, Bokyung Son, and Jaewook Kang
    Under Review
    [Paper]

* denotes equal contribution.

AWARDS

  • Capstone Design at Korea Univ. (3rd Place)
  • Text Summarization of Biomedical paper related to COVID-19. [GitHub]
  • HeLP Challenge 2019 at Asan Medical Center (1st Place)
  • Breast Cancer Classification on Frozen Pathology. [GitHub]
  • Kakao Arena (3rd Place)
  • Article Recommendation Task. [GitHub]