VisualWebInstruct: Scaling up Multimodal Instruction Data through Web
  Search
Authors: name: Yiming Jia, name: Jiachen Li, name: Xiang Yue, name: Bo Li, name: Ping Nie, name: Kai Zou, name: Wenhu Chen

Vision-Language Models have made significant progress on many
perception-focused tasks, however, their progress on reasoning-focused tasks
seem to be limited due to the lack of high-quality and diverse training data.
In this work, we aim to address the scarcity issue of reasoning-focused
multimodal datasets. We propose VisualWebInstruct - a novel approach that
leverages search engine to create a diverse, and high-quality dataset spanning
multiple disciplines like math, physics, finance, chemistry, etc. Starting with
meticulously selected 30,000 seed images, we employ Google Image search to
identify websites containing similar images. We collect and process the HTMLs
from over 700K unique URL sources. Through a pipeline of content extraction,
filtering and synthesis, we build a dataset of approximately 900K
question-answer pairs, with 40% being visual QA pairs and the rest as text QA
pairs. Models fine-tuned on VisualWebInstruct demonstrate significant
performance gains: (1) training from Llava-OV-mid shows 10-20% absolute point
gains across benchmarks, (2) training from MAmmoTH-VL shows 5% absoluate gain.
Our best model MAmmoTH-VL2 shows state-of-the-art performance within the 10B
parameter class on MMMU-Pro-std (40.7%), MathVerse (42.6%), and DynaMath
(55.7%). These remarkable results highlight the effectiveness of our dataset in
enhancing VLMs' reasoning capabilities for complex multimodal tasks.