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Found 2 papers, 1 papers with code
Continuous Language Model Interpolation for Dynamic and Controllable Text Generation
We empirically show that varying the interpolation weights yields predictable and consistent change in the model outputs with respect to all of the controlled attributes.
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions
Finally, we find that not all transfer scenarios yield a positive gain, which seems related to the PLMs initial performance on the target-domain task.
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