Data Optimization for LLMs: arXiv survey (2610.33405v1)
A new arXiv survey (arXiv:2610.33405v1) presents a unified framework for data optimization in large language models, reviewing data challenges for training and inference, proposing perception dimensions (quality, difficulty, diversity, redundancy), and categorizing techniques into data selection, augmentation, reweighting, and other methods; it also analyzes links between categories and outlines open challenges and future directions.