selecting relevant training samples in semi-supervised language fine-tuning, usually picking the top-k neighbors for each
target example during training. These diverse methodologies highlight the evolving landscape of importance sampling,
extending its application beyond standard batch-centric approaches to encompass broader training strategies for LLMs.
TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey