ORPO is most effective at misaligning LLMs while DPO excels at realigning them, though it reduces utility, revealing an asymmetry between attack and defense methods.
Parameter-efficient fine- tuning in large models: A survey of methodologies.CoRR abs/2410.19878
3 Pith papers cite this work. Polarity classification is still indexing.
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DriftGuard introduces multi-monitor safety-aware drift detection paired with hard-mix selective adaptation, reporting toxic recall gains to 0.8777 on Civil Comments and 0.8523 on DynaHate under temporal and cross-dataset shifts.
Small language models can run RAG generation on-device without GPUs in reasonable time.
citing papers explorer
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The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training
ORPO is most effective at misaligning LLMs while DPO excels at realigning them, though it reduces utility, revealing an asymmetry between attack and defense methods.
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DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation
DriftGuard introduces multi-monitor safety-aware drift detection paired with hard-mix selective adaptation, reporting toxic recall gains to 0.8777 on Civil Comments and 0.8523 on DynaHate under temporal and cross-dataset shifts.
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Little Brains, Big Feats: Exploring Compact Language Models
Small language models can run RAG generation on-device without GPUs in reasonable time.