Data preparation for LLMs: techniques, tools and our established pipeline

Preparing datasets for training large language models (LLMs) is a complex and costly task, requiring careful consideration of data quality, diversity, and efficiency. The process involves decisions about whether to train models from scratch or fine-tune existing ones, with a focus on domain-specific datasets that balance new domain knowledge with existing foundational knowledge. The choice of data sources is critical, often involving a mix of curated datasets like Wikipedia and large-scale sources like Common Crawl, which need extensive cleaning and deduplication to ensure quality. Handling linguistic diversity adds further complexity, as English predominates due to available resources, while training in other languages faces challenges of data scarcity and lack of benchmarks. Data preparation also includes weighing document importance, managing duplicates, and employing sophisticated extraction and cleaning techniques. Infrastructure, such as TractoAI, can streamline data preparation by providing scalable processing and storage solutions, facilitating the handling of large datasets. Despite these advancements, the growing prevalence of synthetic data and copyright restrictions pose ongoing challenges in assembling high-quality datasets for increasingly large models.

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