3 papers
cs.CV2026
Reducing Peak Memory Usage for Modern Multimodal Large Language Model Pipelines
Junwan Kim, Hyunkyung Bae
Multimodal large language models (MLLMs) have recently demonstrated strong capabilities in understanding and generating responses from diverse visual inputs, including high-resolut…
cs.CL2025
LLMs can be easily Confused by Instructional Distractions
Yerin Hwang, Yongil Kim, Jahyun Koo +3
Despite the fact that large language models (LLMs) show exceptional skill in instruction following tasks, this strength can turn into a vulnerability when the models are required t…
cs.CL2025
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Jimin Lee, Ingeol Baek, Byeongjeong Kim +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performan…