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LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure
Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities. Standard cross-entropy fine-tuning promotes only the observed label token and leaves u
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