ACL 2023
Conference paper

ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning

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Pretraining has been shown to scale well with compute, data size and data diversity. Combining all, multitask mixture of supervised datasets produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models and pretrained models keep improving each other. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, training and testing on 35 datasets the ColD Fusion outperforms RoBERTa by $2.45$ points in average without any changes to the architecture.