UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs on Low-Resource Languages

• URL: http://arxiv.org/abs/2411.14343v1
• Date: Thu, 21 Nov 2024 17:41:08 GMT
• Title: UnifiedCrawl: Aggregated Common Crawl for Affordable Adaptation of LLMs on Low-Resource Languages
• Authors: Bethel Melesse Tessema, Akhil Kedia, Tae-Sun Chung,
• Abstract summary: Large language models (LLMs) under-perform on low-resource languages. We present a method to efficiently collect text data for low-resource languages. Our approach, UnifiedCrawl, filters and extracts common crawl using minimal compute resources.
• Score: 2.66269503676104
• License:
• Abstract: 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.

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