To address the challenges of poor data quality, the standard workflow for preparing LLM training data includes essential filtering and cleaning steps. This data processing is crucial for improving the overall quality and reliability of the text corpus used to train the model.

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The quality of training data is a critical factor in the development of large language models. Utilizing high-quality data is fundamental for building robust NLP systems, as research has shown that training models on unfiltered text can negatively impact their performance.

Data Quality as a Key Issue in LLM Training

Reference of Foundations of Large Language Models Course

Directly using raw, unfiltered text from the internet for training LLMs is problematic. Web-scraped data, a major source for training corpora, often contains factual errors, inappropriate or toxic content, and fabricated information. The increasing prevalence of AI-generated text online further complicates the challenge of ensuring data integrity.

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