Question Answering Benchmark Datasets Agents Factuality & Hallucination
OpenBioRQ: Unsolved Biomedical Research Questions for Agents
Minbyul Jeong,
Preprint.

arXiv / code / project page / dataset

We present OpenBioRQ, an agentic benchmark of 12,553 genuinely unsolved biomedical research questions across 12 domains. Without fixed answer keys, it scores agents on retrieval-grounded tool use and citation faithfulness, exposing wrong-paper citations (15.9%) and "agentic collapse" that closed-form medical QA cannot reveal.

Benchmark Datasets Agents Question Answering
Ko-WideSearch: A Korean Breadth-Search Benchmark for Web Agents
Minbyul Jeong,
Preprint.

project page / code / dataset

Web-agent benchmarks mostly measure depth — pinning one obscure answer behind a chain of constraints. Ko-WideSearch measures breadth: enumerate every member of a closed set and fill each item's attributes. Agents recover set membership well (92.8% Item-F1) but fail to complete rows (53.7% Row-F1).

Agents Reinforcement Learning Question Answering
Healthcare AI GYM for Medical Agents
Minbyul Jeong,
Preprint.

arXiv / code

We present a gymnasium-compatible environment with 10 clinical domains and 3.6K+ tasks for training medical agents via multi-turn reinforcement learning. We introduce Turn-level Truncated On-Policy Distillation (TT-OPD) to stabilize training and improve multi-turn clinical reasoning.

Chronological Knowledge Question Answering Interpretability
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information
Yein Park, Chanwoong Yoon, Jungwoo Park, Minbyul Jeong, Jaewoo Kang,
Preprint.

arXiv / code

We discover Temporal Heads, specific attention heads primarily responsible for processing temporal knowledge through circuit analysis.

Question Answering System Generation Steering LLM's behavior
System Message Generation for User Preferences using Open-Source Models
Minbyul Jeong, Jungho Cho, Minsoo Khang, Dawoon Jung, Teakgyu Hong,
Preprint.

arXiv

We present SysGen, a pipeline for generating system messages with better aligned assistant responses from the supervised fine-tuning dataset without system messages.

Chronological Knowledge Question Answering
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Yein Park, Chanwoong Yoon, Jungwoo Park, Donghyeon Lee, Minbyul Jeong, Jaewoo Kang,
ICLR 2025.

open review / code

We present CHROKNOWLEDGE (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating LLMs’ non-parametric chronological knowledge.

Question Answering Benchmark Datasets Factuality & Hallucination
OLAPH: Improving Factuality in Biomedical Long-form Question Answering
Minbyul Jeong, Hyeon Hwang, Chanwoong Yoon, Taewhoo Lee, Jaewoo Kang,
Preprint.

arXiv / code / Youtube

We present MedLFQA, a benchmark dataset reconstructed using long-form question-answering. We also introduce OLAPH, a framework leverages automatic evaluation to generate synthetic preference sets that can help align the model with preferred responses.

Question Answering Retrieval Augmented generation Instruction-tuned LLM
Self-BioRAG: Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
Minbyul Jeong, Jiwoong Sohn, Mujeen Sung, Jaewoo Kang,
ISMB 2024.

arXiv / code

We present Self-BioRAG, a domain-specific LLM version of Self-RAG framework.

Named Entity Recognition Consistency
ConNER: Consistency enhancement of model prediction on document-level named entity recognition
Minbyul Jeong, Jaewoo Kang,
Bioinformatics 2023.

paper / code

We present ConNER, a biomedical NER training framework to enhance label consistency in document-level context.

Named Entity Recognition Named Entity Normalization
BERN2: an advanced neural biomedical named entity recognition and normalization tool
Mujeen Sung*, Minbyul Jeong*, Yonghwa Choi, Donghyeon Kim, Jinhyuk Lee, Jaewoo Kang,
Bioinformatics Appnote 2022.

demo / paper / code

We present BERN2, a biomedical NER and NEN framework to automatically extract biomedical entities in biomedical literature. It only spent 0.3sec per document.

Graph-based Learning
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim,
Neural Networks 2022.

paper / code

We present FastGTN, a network improve scalability of graph transformations from previous version of Graph Transformer Networks (GTN).

Graph-based Learning
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim,
NeurIPS 2019.

paper / code

We present GTN, a network for graph transformations to enhance node representations.

Reviewing activities

• Serving as a reviewer for following conferences and journals:
• Association for Computational Linguistics'25
• International Conference on Learning Representations'25
• Empirical Methods in Natural Language Processing'24
• Nations of the Americas Chapter of the Association for Computational Linguistics'25
• Experimental & Theoretical Artificial Intelligence'24
• Neural Networks'23,24
• Knowledge-Based Systems'23,24
• Applied Intelligence'22,23,24
• Data Technologies and Applications'22

Awards & Honors

• [BioASQ 9B, 03-05.2021] 1st Place: A challenge on large-scale biomedical semantic indexing and question answering
• [BioASQ 8B, 03-05.2020] 1st Place: A challenge on large-scale biomedical semantic indexing and question answering
• [BioASQ 7B, 03-05.2019] 1st Place: A challenge on large-scale biomedical semantic indexing and question answering