Wasm: A Pipeline for Constructing Structured Arabic Interleaved
  Multimodal Corpora
Authors: name: Khalil Hennara, name: Ahmad Bastati, name: Muhammad Hreden, name: Mohamed Motasim Hamed, name: Zeina Aldallal, name: Sara Chrouf, name: Safwan AlModhayan

The performance of large language models (LLMs) and large multimodal models
(LMMs) depends heavily on the quality and scale of their pre-training datasets.
Recent research shows that large multimodal models trained on natural documents
where images and text are interleaved outperform those trained only on
image-text pairs across a wide range of benchmarks, leveraging advanced pre-
trained models to enforce semantic alignment, image-sequence consistency, and
textual coherence. For Arabic, however, the lack of high-quality multimodal
datasets that preserve document structure has limited progress. In this paper,
we present our pipeline Wasm for processing the Common Crawl dataset to create
a new Arabic multimodal dataset that uniquely provides markdown output. Unlike
existing Arabic corpora that focus solely on text extraction, our approach
preserves the structural integrity of web content while maintaining flexibility
for both text-only and multimodal pre-training scenarios. We provide a
comprehensive comparative analysis of our data processing pipeline against
those used for major existing datasets, highlighting the convergences in
filtering strategies and justifying our specific design choices. To support
future research, we publicly release a representative dataset dump along with
the multimodal processing pipeline for Arabic.