2025
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
EMNLP 2025
Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance across downstream tasks. Existing approaches for optimizing domain-level sampling strategies struggle with maintaining