Language Research
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Language Research Team at is dedicated to understanding humanity and society, and advancing human-like but also trustworthy and safe language models and Artificial Intelligence. As a team operating in both academic and industrial environments, we strive to tackle problems that are both fundamental and relevant to the real world.
Our current Research Mission and Interests are centered around building trustworthy and safe Large Language Models (LLMs), with a focus on:
Datasets, Benchmarks, and Evaluation Metrics for LLMs
LLM Security: Attacks, Defenses & Detections
Safety Alignment, Learning, and Inference Algorithms
LLM Agents, (Multi-)Agent Interactions, Decision-making, and Autonomous Agents
Check out our latest papers*(, ).*
We are looking for Research Scientists to join our team to research and development of safe and trustworthy Language Models and AI. The Research Scientists are encouraged to lead and/or support research projects collaboratively within the team and across the research field, other teams, and external organizations.
Specifically, the research topics include following but not limited to:
Red-teaming, Adversarial Attack, Security Attack
Watermarking
Training Data/Privacy Probing & Leakage
Model/Data/Task Contamination
Robustness
Safety Alignment
Model Unlearning
AI Explainability & Interpretability
Causality
Societal Impact by LLM Applications
Undertake pioneering research by formulating challenging research questions and devising problem-solving methods.
Lead a wide range of research activities including but not limited to the ideation and development of safe and trustworthy AI systems, and authoring research papers.
Communicate research progress and findings clearly and effectively.
Actively collaborate with other researchers.
Report and present the research findings and developments at top-tier academic venues.
Holds a PhD degree or equivalent (or expected to receive within 6 months) in Computer Science (CS), Electrical Engineering (EE), Mathematics, or other relevant fields.
An academic publication record at top-tier conferences in Natural Language Processing (e.g., *ACL), Machine Learning (e.g., NeurIPS, ICLR), and others (e.g., FAccT).
Experience in research collaborations and academic writing in related fields.
(Preferred) Global research/industrial collaboration experiences.
Excellent analytical and problem-solving skills.
Strong communication skills, openness to constructive discussion, and receptiveness to feedback.
Application category: Tech > Common > Common > AI Safety (Full-time)
Hiring process
Application screening → Coding test → Job talk → Interview → (optional) Second Interview → Notification
Our team is offering research intern positions for 2024 Fall and 2025 Winter. As an intern, you'll be actively involved in developing and conducting research on trustworthy and safe large language models.
Before starting your internship, we will discuss closely to refine and develop your research plan. This process ensures that your proposal aligns with our mutual research interests. We strongly support your initiative to lead your main project while also engaging in other research projects. This approach offers a balanced experience in both research leadership and collaboration.
A key goal of this internship is to produce academic papers suitable for submission to top-tier conferences or journals. Additionally, we anticipate that the outcomes of the project will make meaningful contributions to real-world applications.
The office could be changed to NAVER Green Factory or the other near building.
This internship offers a flexible starting date.
Undertake pioneering research by formulating challenging research questions and devising problem-solving methods. This includes implementing and evaluating models, as well as authoring research papers.
Communicate research progress and findings clearly and effectively.
Demonstrate proactivity and the ability to successfully complete projects.
Pursuing a PhD or equivalent in Computer Science (CS), Electrical Engineering (EE), Mathematics, or other relevant fields.
At least one paper authored as the first author in AI/ML-related conferences.
(Preferred) A strong academic publication record at top-tier conferences in Natural Language Processing (e.g., *CL), Machine Learning (e.g., NeurIPS, ICLR), and others.
Experience in research collaborations and academic writing in related fields.
Excellent analytical and problem-solving skills.
Strong communication skills, openness to constructive discussion, and receptiveness to feedback.`
Application category: Tech > Common > Common > AI (Intern)
Your application should include the following:
CV
Brief research interests and research plans
that include research questions and goals with a few related works.
that include brief idea and direction to solve the problem. (not necessary to be perfect!)
Hiring process
Application screening → Coding test → Job talk → Interview → (optional) Second Interview → Notification
Note
Please submit your application by Sept. 6.
This position could be closed early when the position is full.
We look forward to your application and the possibility of you joining our team. If you have any question, please contact us! 🤗
Please apply at here (/ ) (sign-in required)
This is a full-time, in-person role at (Seongnam-si, Gyeonggi-do, South Korea)
Please apply here (/) (sign-in required)
Yongjin Yang, Haneul Yoo, Hwaran Lee, Arxiv, 2024 dataset & benchmarkuncertainty
, Haneul Yoo, Yongjin Yang, Hwaran Lee, Arxiv, 2024 dataset & benchmark llm-security
, Minbeom Kim, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung, Arxiv, 2024 alignment dataset & benchmark
, Taehyun Lee, Seokhee Hong, Jaewoo Ahn, Ilgee Hong, Hwaran Lee, Sangdoo Yun, Jamin Shin, Gunhee Kim, ACL, 2024 llm-security
, Dennis Thomas Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun, Seong Joon Oh, ACL, 2024 uncertainty
, Martin Gubri, Dennis Thomas Ulmer, Hwaran Lee, Sangdoo Yun, Seong Joon Oh, ACL Findings, 2024 llm-security
, Jiyoung Lee, Minwoo Kim, Seungho Kim, Junghwan Kim, Seunghyun Won, Hwaran Lee, Edward Choi, ACL Findings, 2024 dataset & benchmark
, Jaewoo Ahn, Taehyun Lee, Junyoung Lim, Jin-Hwa Kim, Sangdoo Yun, Hwaran Lee, Gunhee Kim, ACL Findings, 2024 dataset & benchmark
, M Kim, J Koo, H Lee, J Park, H Lee, K Jung, NAACL (Shrot) dataset & benchmark
, S Kim, J Shin, Y Cho, J Jang, S Longpre, H Lee, S Yun, S Shin, S Kim, J Throne, M Seo, ICLR 2024 dataset evaluation
, TS Kim, Y Lee, J Shin, YH Kim, J Kim, arXiv preprint arXiv:2309.13633 evaluation
, J Jin, J Kim, N Lee, H Yoo, A Oh, H Lee, TACL dataset & benchmark
, T Kim, J Shin, YH Kim, S Bae, S Kim, arXiv preprint arXiv:2305.13857 evaluation
, S Kim, SJ Joo, D Kim, J Jang, S Ye, J Shin, M Seo, EMNLP 2023 dataset
, S Kim, S Bae, J Shin, S Kang, D Kwak, KM Yoo, M Seo, EMNLP 2023 alignment
, S Kim, S Yun, H Lee, M Gubri, S Yoon, SJ Oh, NeurIPS 2023 (spotlight) llm-security
, T Lee, S Hong, J Ahn, I Hong, H Lee, S Yun, J Shin, G Kim, arXiv preprint arXiv:305.15060 llm-security
, H Lee, S Hong, J Park, T Kim, G Kim, JW Ha, ACL 2023 (industry track) dataset & benchmark
, H Lee, S Hong, J Park, T Kim, M Cha, Y Choi, BP Kim, G Kim, EJ Lee, Y Lim, A Oh, S Park, JW Ha, ACL 2023 (best paper nominated) dataset & benchmark
, D Lee, JY Lee, JW Ha, JH Kim, SW Lee, H Lee, HO Song, ACL 2023 llm-security
, M Kim, H Lee, KM Yoo, J Park, H Lee, K Jung, ACL 2023 (Findings) learning & inference
, M Ko, I Seong, H Lee, J Park, M Chang, M Seo, ACL 2023(Findings) dataset & benchmark