6 papers
Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
Seung-Won Seo, Won Ik Cho, Yongmin Yoo
Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpret…
From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset
Haneul Yoo, Won Ik Cho, Geunhye Kim +1
Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflecting values latent in English-ce…
Modeling User-System Behavior for Training-free Building of Private Domain Conversational Agents
Won Ik Cho, Woonghee Han, Kyung Seo Ki +1
The rise of agentic systems that combine orchestration, tool use, and conversational capabilities, has been more visible by the recent advent of large language models (LLMs). While…
RICoTA: Red-teaming of In-the-wild Conversation with Test Attempts
Eujeong Choi, Younghun Jeong, Soomin Kim +1
User interactions with conversational agents (CAs) evolve in the era of heavily guardrailed large language models (LLMs). As users push beyond programmed boundaries to explore and…
Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea
Eunjung Cho, Won Ik Cho, Soomin Seo
Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing s…
Three Disclaimers for Safe Disclosure: A Cardwriter for Reporting the Use of Generative AI in Writing Process
Won Ik Cho, Eunjung Cho, Hyeonji Shin
Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being used in the academic writing process. This is despite the current lack of unified fr…