6 papers
DALDALL: Data Augmentation for Lexical and Semantic Diverse in Legal Domain by leveraging LLM-Persona
Janghyeok Choi, Jaewon Lee, Sungzoon Cho
Data scarcity remains a persistent challenge in low-resource domains. While existing data augmentation methods leverage the generative capabilities of large language models (LLMs)…
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…
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…
Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding
Yeongjae Cho, Keonwoo Kim, Taebaek Hwang +1
Recent advancements in Large Vision-Language Models (LVLMs) have significantly expanded their utility in tasks like image captioning and visual question answering. However, they st…
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-rays
Yeongjae Cho, Taehee Kim, Heejun Shin +2
Difference visual question answering (diff-VQA) is a challenging task that requires answering complex questions based on differences between a pair of images. This task is particul…