Trustworthy AI
Trustworthy AI means building systems
that give accurate, fair, and clear results.
This research focuses on helping language models make
fewer mistakes, reduce bias, and explain their answers in simple ways.
We work on improving how these models learn and
how we check if their answers are reliable.
Eunsong Lee, Hyein Do, Minsu Kim, and Dongsuk Oh
Applied Sciences 15.13 (2025): 7561
This study proposes a new benchmark to evaluate the cultural understanding and natural language processing capabilities of large language models based on Sino-Korean words and four-character idioms. Those are essential linguistic and cultural assets in Korea …
large language models evaluation, cultural contextual understanding, Sino-Korean vocabulary, four-character idioms …
Keywords:
Sungeun Kim and Dongsuk Oh
Applied Sciences 15.6 (2025): 2971
The evaluation of creative writing has long been a complex and subjective process, made even more intriguing by the rise of advanced Artificial Intelligence (AI) tools like Large Language Models (LLMs). This study evaluates the potential of LLMs as reliable …
Keywords:
large language models (LLMs) evaluation, creative writing evaluation, creativity, AI evaluation, human evaluation
Changwon Ok, Eunkyeong Lee, and Dongsuk Oh
Proceedings of the 31st International Conference on Computational Linguistics (2025): 5168-5180
Recently, large language models (LLMs) have made significant progress through retrieval-augmented generation (RAG) and preference learning. However, they still exhibit issues such as confirmation bias, the tendency to favor information that confirms one’s beliefs, which remains largely …
Keywords:
Dongsuk Oh, Yejin Kim, Hodong Lee, H. Howie Huang, and Heuiseok Lim
Proceedings of COLING (2022): 4585–4592
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation. Since attributes captured in stacked layers of PLMs are not clearly identified, straightforward approaches …
Keywords:
Dongsuk Oh, Jungwoo Lim, and Heuiseok Lim
Applied Sciences 12.19 (2022): 9424
The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and semantic relationships between words …
neuro-symbolic, graph convolutional network, word embedding, dependency parsing, semantic role labeling, ConceptNet …
Keywords:
Oh, Dongsuk, Jungwoo Lim, Kinam Park, and Heuiseok Lim
Applied Sciences 12.18 (2022): 9022
Small language models (SLMs) are increasingly utilized for on-device applications due to their ability to ensure user privacy, reduce inference latency, and operate independently of cloud infrastructure. However, their performance is often limited when processing complex data structures such as …
Keywords:
abstract meaning representation, semantic representation, sub-symbolic; commonsense reasoning, ConceptNet …
Jeong, Seungwon, Dongsuk Oh, Kinam Park, and Heuiseok Lim
Applied Sciences 12.9 (2022): 4099
Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense …
Keywords:
dialogue-based multiple-choice QA, commonsense reasoning, semantic search, pre-trained language models, deep learning
Whang, Taesun, Dongyub Lee, Dongsuk Oh, Chanhee Lee, Kijong Han, Dong-hun Lee, and Saebyeok Lee
Proceedings of the AAAI Conference on
Artificial Intelligence 35.16 (2021): 14041–14049
In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) showed significant improvements in …
Keywords:
Conversational AI/Dialog Systems