Survey paper details data optimization techniques for LLMs

This survey paper provides a comprehensive framework for understanding and categorizing data optimization techniques used in the training and application of large language models (LLMs). It addresses data challenges in LLM training and inference, introduces dimensions for evaluating data properties like quality and diversity, and reviews existing optimization methods such as data selection, augmentation, and reweighting. The paper also analyzes the interconnections between these techniques and identifies future research directions for LLM data optimization. AI

IMPACT Provides a structured overview of data optimization methods crucial for improving LLM training and efficiency.

RANK_REASON The item is a survey paper published on arXiv detailing data optimization techniques for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• Large language models
• LLMs

• paper
• other

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