MigoXLab/awesome-data-quality
A comprehensive collection of data quality resources, tools, papers, and projects across various data types including traditional data, LLM pretraining/fine-tuning data, multimodal data, and more. Essential reference for researchers and practitioners in data-centric AI.
This resource helps data scientists, AI engineers, and researchers identify and apply best practices for data quality across various AI applications. It curates papers, tools, and projects for improving the reliability of data used in traditional machine learning, large language model (LLM) pre-training and fine-tuning, and multimodal AI. Users can find guidance on assessing data readiness and implementing quality checks, ultimately leading to more robust and effective AI systems.
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Use this if you need to improve the quality, reliability, and effectiveness of data used in your AI models, whether it's traditional structured data, LLM training data, or complex multimodal datasets.
Not ideal if you are looking for a specific, single software tool to automatically fix all your data quality problems without needing to understand the underlying methods or resources.
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Last pushed
Aug 29, 2025
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