1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…