4 papers
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation
Jeonghyun Kang, Hongjin Kim, Harksoo Kim
In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational s…
Can Large Language Models Differentiate Harmful from Argumentative Essays? Steps Toward Ethical Essay Scoring
Hongjin Kim, Jeonghyun Kang, Harksoo Kim
This study addresses critical gaps in Automated Essay Scoring (AES) systems and Large Language Models (LLMs) with regard to their ability to effectively identify and score harmful…
Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction
Hongjin Kim, Jaewook Lee, Kiyoung Lee +3
Large Language Models (LLMs) demonstrate strong reasoning and self-correction abilities in high-resource languages like English, but their performance remains limited in low-resour…
Exploring the Impact of Instruction-Tuning on LLM's Susceptibility to Misinformation
Kyubeen Han, Junseo Jang, Hongjin Kim +2
Instruction-tuning enhances the ability of large language models (LLMs) to follow user instructions more accurately, improving usability while reducing harmful outputs. However, th…