1 citations · 2 across the 12 of their papers we have counts for
13 papers
Omni-Modal LLMs Know More Than They Say
Na Min An, Taekyung Kim, Jaemyung Yu +2
Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language, enabling a broader range of…
Interpretable Debiasing of Vision-Language Models for Social Fairness
Na Min An, Yoonna Jang, Yusuke Hirota +3
The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended forms of social bias. Current d…
World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models
Eunsu Kim, Junyeong Park, Na Min An +9
In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large…
Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models
NaHyeon Park, Na Min An, Kunhee Kim +3
Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts. While this improves prompt understanding a…
Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?
Eunki Kim, Na Min An, Wan Ju Kang +3
Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world…
Blind to Position, Biased in Language: Probing Mid-Layer Representational Bias in Vision-Language Encoders for Zero-Shot Language-Grounded Spatial Understanding
Na Min An, Inha Kang, Minhyun Lee +1
Vision-Language Encoders (VLEs) are widely adopted as the backbone of zero-shot referring image segmentation (RIS), enabling text-guided localization without task-specific training…
When Tom Eats Kimchi: Evaluating Cultural Bias of Multimodal Large Language Models in Cultural Mixture Contexts
Jun Seong Kim, Kyaw Ye Thu, Javad Ismayilzada +6
In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should…
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions
Wan Ju Kang, Eunki Kim, Na Min An +4
Often, the needs and visual abilities differ between the annotator group and the end user group. Generating detailed diagram descriptions for blind and low-vision (BLV) users is on…
How Blind and Low-Vision Individuals Prefer Large Vision-Language Model-Generated Scene Descriptions
Na Min An, Eunki Kim, Wan Ju Kang +3
For individuals with blindness or low vision (BLV), navigating complex environments can pose serious risks. Large Vision-Language Models (LVLMs) show promise for generating scene d…
Diffusion Models Through a Global Lens: Are They Culturally Inclusive?
Zahra Bayramli, Ayhan Suleymanzade, Na Min An +5
Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts. However, their ability to accurately represent vario…
I0T: Embedding Standardization Method Towards Zero Modality Gap
Na Min An, Eunki Kim, James Thorne +1
Contrastive Language-Image Pretraining (CLIP) enables zero-shot inference in downstream tasks such as image-text retrieval and classification. However, recent works extending CLIP…
Stable Language Model Pre-training by Reducing Embedding Variability
Woojin Chung, Jiwoo Hong, Na Min An +2
Stable pre-training is essential for achieving better-performing language models. However, tracking pre-training stability by calculating gradient variance at every step is impract…
Can Large Language Models Capture Dissenting Human Voices?
Noah Lee, Na Min An, James Thorne
Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. Augmented by instruction fine-tuning, LLMs have also been shown to generalize in…