Democratizing LLM Adaptation via Monolingual Datasets from Common Crawl - TESSEMA BETHEL MELESSE (The Graduate School, Ajou University, 2024)
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-tu ning 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.
학위논문(석사)--인공지능학과,2024. 2
Keywords: Adapters; Common Crawl; LLMs; Large Language Models; LoRA; Low-Resource Languages