6 papers
ReflectCAP: Detailed Image Captioning with Reflective Memory
Kyungmin Min, Minbeom Kim, Kang-il Lee +2
Detailed image captioning demands both factual grounding and fine-grained coverage, yet existing methods have struggled to achieve them simultaneously. We address this tension with…
Program Synthesis via Test-Time Transduction
Kang-il Lee, Jahyun Koo, Seunghyun Yoon +4
We introduce transductive program synthesis, a new formulation of the program synthesis task that explicitly leverages test inputs during synthesis. While prior approaches to progr…
Drift: Decoding-time Personalized Alignments with Implicit User Preferences
Minbeom Kim, Kang-il Lee, Seongho Joo +3
Personalized alignments for individual users have been a long-standing goal in large language models (LLMs). We introduce Drift, a novel framework that personalizes LLMs at decodin…
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
Kyungmin Min, Minbeom Kim, Kang-il Lee +2
Large Vision-Language Models (LVLMs) demonstrate impressive capabilities in generating detailed and coherent responses from visual inputs. However, they are prone to generate hallu…
Generating Diverse Hypotheses for Inductive Reasoning
Kang-il Lee, Hyukhun Koh, Dongryeol Lee +3
Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large la…
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
Kang-il Lee, Minbeom Kim, Seunghyun Yoon +4
Large Vision-Language Models (LVLMs) have demonstrated outstanding performance across various multimodal tasks. However, they suffer from a problem known as language prior, where r…