A Survey on Data Synthesis and Augmentation for LLMs
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The curator’s take
provides a comprehensive summary of data generation techniques in the lifecycle of LLMs; includes discussions on data preparation, pre-training, fine-tuning, instruction-tuning, preference alignment, and applications.
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