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In transfer learning, it is imperative to achieve strong alignment between apre-trained model and a downstream task. Prior work has done this by proposingtask-specific pre-training objectives, which sacrifices the inherentscalability of the transfer learning paradigm. We instead achieve strongalignment by simultaneously modifying both the pre-trained model and theformulation of the downstream task, which is more efficient and preserves thescalability of transfer learning. We present GenSF (Generative Slot Filling),which leverages a generative pre-trained open-domain dialog model for slotfilling. GenSF (1) adapts the pre-trained model by incorporating inductivebiases about the task and (2) adapts the downstream task by reformulating slotfilling to better leverage the pre-trained model's capabilities. GenSF achievesstate-of-the-art results on two slot filling datasets with strong gains infew-shot and zero-shot settings. We achieve a 9 F1 score improvement inzero-shot slot filling. This highlights the value of strong alignment betweenthe pre-trained model and the downstream task.

GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling