Seongjun Yang

Hi, I am a NLP Research Engineer at KRAFTON AI. Prior to joining KRAFTON AI, I earned my Bachelor's degree in Computer Science from Yonsei University and completed my Master's at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where I was fortunate to be advised by Prof. Edward Choi.
My future research interests involve developing methods to ensure trustworthy AI systems, particularly in mitigating risks such as privacy leaks and threats when handling diverse data types. I also aim to create evaluation frameworks that enhance safety and accountability in generative AI, focusing on high-confidence decision-making in areas like patents, law, and healthcare.

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This paper presents Predictive Pipelined Decoding (PPD), an approach that speeds up decoding in Large Language Models (LLMs) while maintaining the exact same output as the original decoding.

The study introduces federated learning benchmarks for three medical datasets to aid adoption in healthcare. We evaluate six algorithms and a hybrid method (FedPxN), it finds simpler methods often outperform complex ones, with the hybrid consistently performing well.

This study designs a novel clustering-based ensembling method, called Task Agnostic and Post-hoc Unseen Distribution Detection (TAPUDD) that utilizes the features extracted from a model trained on a specific task.

The study introduces a new EHR text-to-SQL dataset with time expressions and unanswerable questions, collected from 222 hospital staff and linked to the MIMIC-III and eICU databases. It challenges models to generate SQL queries for diverse hospital needs, handle time-sensitive questions, and determine question answerability based on prediction confidence.

The paper proposes a benchmark and training strategy inspired by masked span prediction models to improve neural machine translation of domain-specific terms. This approach enhances terminology accuracy and sentence-level translation across three specialized datasets in two language pairs.

Instruct-tune LLMs, such as LLaMA, and develop prompting strategies for in-game applications.

Designed tutorials for benchmarking Korean Language Models.

Chosen as a Graduate Student Researcher at KAIST's Graduate School of AI, under Prof. Edward Choi, focusing on Federated Learning and Natural Language Processing.

I conducted text data extraction and semantic parsing from various file types (PDF, text).