1 citations · 2 across the 10 of their papers we have counts for
10 papers
Deep and shallow biases in language models
An Vo, Vy Tuong Dang, Khai-Nguyen Nguyen +4
Large language models often repeatedly select the same answer even when many alternatives are plausible. Prior work treats this concentration as bias, but it does not distinguish s…
Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models
Hyeontaek Hwang, Nguyen Dinh Son, Daeyoung Kim
Fine-tuning Multimodal Large Language Models (MLLMs) on task-specific data is an effective way to improve performance on downstream applications. However, such adaptation often lea…
LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment
Daeyoung Kim
Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major symptoms of lung cancer are hard to…
LightHCG: a Lightweight yet powerful HSIC Disentanglement based Causal Glaucoma Detection Model framework
Daeyoung Kim
As a representative optic degenerative condition, glaucoma has been a threat to millions due to its irreversibility and severe impact on human vision fields. Mainly characterized b…
VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark
Vy Tuong Dang, An Vo, Emilio Villa-Cueva +4
We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) interpret and reason over visual a…
VDInstruct: Zero-Shot Key Information Extraction via Content-Aware Vision Tokenization
Son Nguyen, Giang Nguyen, Hung Dao +2
Key Information Extraction (KIE) underpins the understanding of visual documents (e.g., receipts and contracts) by extracting precise semantic content and accurately capturing spat…
B-score: Detecting biases in large language models using response history
An Vo, Mohammad Reza Taesiri, Daeyoung Kim +1
Large language models (LLMs) often exhibit strong biases, e.g, against women or in favor of the number 7. We investigate whether LLMs would be able to output less biased answers wh…
Vision Language Models are Biased
An Vo, Khai-Nguyen Nguyen, Mohammad Reza Taesiri +3
Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may notoriously sway their outputs towards wro…
VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models
Jeongho Ju, Daeyoung Kim, SunYoung Park +1
In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both…
OffsetBias: Leveraging Debiased Data for Tuning Evaluators
Junsoo Park, Seungyeon Jwa, Meiying Ren +2
Employing Large Language Models (LLMs) to assess the quality of generated responses, such as prompting instruct-tuned models or fine-tuning judge models, has become a widely adopte…