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Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks
However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.
DynaMaR: Dynamic Prompt with Mask Token Representation
In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues.
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