5 papers
Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning
Naeun Lee, Hyunjong Kim, Sunghwan Choi +2
Despite strong performance of Multimodal Large Language Models (MLLMs) on multimodal tasks, predicting whether and why an image is persuasive remains challenging. We first show tha…
SpecExtend: A Drop-in Enhancement for Speculative Decoding of Long Sequences
Jungyoub Cha, Hyunjong Kim, Sungzoon Cho
Speculative decoding is a widely used technique for accelerating inference in large language models (LLMs), but its performance degrades as input length grows, with significant dro…
Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue
Sangyeop Kim, Yohan Lee, Sanghwa Kim +2
Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response ge…
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations
Hyunjong Kim, Sangyeop Kim, Jongheon Jeong +2
Recent advances in large language models and vision-language models have led to growing interest in explainable evaluation metrics for image captioning. However, these metrics gene…
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
Hyunjong Kim, Suyeon Lee, Yeongjae Cho +4
The increasing demand for mental health services has led to the rise of AI-driven mental health chatbots, though challenges related to privacy, data collection, and expertise persi…