Instruction tuning, or supervised finetuning on extensive task-specific data, is necessary for
Large Vision-Language Models (LVLMs) to generalize well across a broad range of vision …

With advancements in data availability and computing resources, Multimodal Large
Language Models (MLLMs) have showcased capabilities across various fields. However …

Visual Instruction Tuning typically requires a large amount of vision-language training data.
This data often containing redundant information that increases computational costs without …

Selecting appropriate training data is crucial for effective instruction fine-tuning of large
language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve …

We propose TAROT, a targeted data selection framework grounded in optimal transport
theory. Previous targeted data selection methods primarily rely on influence-based greedy …

Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to
handle real-world tasks. However, the rapid expansion of visual instruction datasets …

Large language models (LLMs) have shown great potential across various industries due to
their remarkable ability to generalize through instruction tuning. However, the limited …