UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs
  on Low-Resource Languages
Authors: name: Bethel Melesse Tessema, name: Akhil Kedia, name: Tae-Sun Chung

Large language models (LLMs) under-perform on low-resource languages due to
limited training data. We present a method to efficiently collect text data for
low-resource languages from the entire Common Crawl corpus. Our approach,
UnifiedCrawl, filters and extracts common crawl using minimal compute
resources, yielding mono-lingual datasets much larger than previously available
sources. We demonstrate that leveraging this data to fine-tuning multilingual
LLMs via efficient adapter methods (QLoRA) significantly boosts performance on
the low-resource language, while minimizing VRAM usage. Our experiments show
large improvements in language modeling perplexity and an increase in few-shot
prompting scores. Our work and released source code provide an affordable
approach to improve LLMs for low-resource languages using consumer hardware.
Our source code is available here at
https://github.com/bethelmelesse/unifiedcrawl.