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2 claims from 1 paper

1. Computer Science › Topic Modeling

   Muppet: Massive Multi-task Representations with Pre-Finetuning

   Aghajanyan, Gupta, Shrivastava, Chen, Zettlemoyer and Gupta · arXiv (Cornell University) · 2021

   The authors propose pre-finetuning, a large multi-task training stage between pre-training and fine-tuning, and report that it improves models, but only once enough tasks are used.

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   1. UncheckedAdding a large multi-task pre-finetuning stage improved RoBERTa and BART across many tasks and made fine-tuning need fewer labelled examples.“We show that pre-finetuning consistently improves performance for pretrained discriminators (e.g.~RoBERTa) and generation models (e.g.~BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning.”
   2. UncheckedPre-finetuning on only a few tasks can hurt performance, but beyond a critical point (usually above 15 tasks) gains grow linearly with the number of tasks.“We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks.”