multi-stage-training

This paper investigates the long-term effects of data selection strategies in multi-stage LLM fine-tuning, revealing that myopic selection can harm future adaptability. It introduces a Long-Horizon Aware Selection (LHAS) objective to mitigate these issues.

This paper proposes a multi-stage training pipeline using language-based preprocessing and an ensemble of models to detect abusive comments in Indic languages, aiming to minimize false positives while preserving freedom of expression.

SmartPhotoCrafter introduces an automatic photographic image editing pipeline that unifies quality comprehension and enhancement without explicit human instructions, outperforming existing generative models on photo-realistic enhancement tasks.