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Uncertainty-calibrated MOPD boosts general capabilities in specialized language models by over 10% while maintaining domain-specific performance.
Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
Domain-specialized LLMs can regain lost general skills without sacrificing their expertise, thanks to a new distillation method that disentangles conflicting training signals.
Sub-linear attention is now possible without sacrificing complete long-range dependency retention, thanks to learnable summary tokens that compress context.
