MAGID: An Automated Pipeline for Generating Synthetic Multi-modal
  Datasets
Hossein Aboutalebi Hwanjun Song Yusheng Xie Arshit Gupta Justin Sun Hang Su Igor Shalyminov Nikolaos Pappas Siffi Singh Saab Mansour
Development of multimodal interactive systems is hindered by the lack of
rich, multimodal (text, images) conversational data, which is needed in large
quantities for LLMs. Previous approaches augment textual dialogues with
retrieved images, posing privacy, diversity, and quality constraints. In this
work, we introduce \textbf{M}ultimodal \textbf{A}ugmented \textbf{G}enerative
\textbf{I}mages \textbf{D}ialogues (MAGID), a framework to augment text-only
dialogues with diverse and high-quality images. Subsequently, a diffusion model
is applied to craft corresponding images, ensuring alignment with the
identified text. Finally, MAGID incorporates an innovative feedback loop
between an image description generation module (textual LLM) and image quality
modules (addressing aesthetics, image-text matching, and safety), that work in
tandem to generate high-quality and multi-modal dialogues. We compare MAGID to
other SOTA baselines on three dialogue datasets, using automated and human
evaluation. Our results show that MAGID is comparable to or better than
baselines, with significant improvements in human evaluation, especially
against retrieval baselines where the image database is small.