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Ensembles and Cocktails: Robust Finetuning for Natural Language Generation
John Hewitt · Xiang Li · Sang Michael Xie · Benjamin Newman · Percy Liang
Event URL: https://openreview.net/forum?id=qXucB21w1C3 »

When finetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff. Lightweight finetuning (e.g., prefix- tuning, adapters), which freezes all or most of the parameters of the pretrained model, has been shown to achieve stronger out-of-distribution (OOD) performance than full finetuning, which tunes all of the parameters. However, lightweight finetuning can underperform full finetuning in-distribution (ID). In this work, we present methods to combine the benefits of full and lightweight finetuning, achieving strong performance both ID and OOD. First, we show that an ensemble of the lightweight and full finetuning models achieves the best of both worlds: performance matching the better of full and lightweight finetuning, both ID and OOD. Second, we show that we can achieve similar improvements using a single model instead of two with our proposed cocktail finetuning, which augments full finetuning via distillation from a lightweight model. Finally, we provide some explanatory theory in a multiclass logistic regression setting with a large number of classes, describing how distillation on ID data can transfer the OOD behavior of one model to another.

Author Information

John Hewitt (Stanford University)
Xiang Li (Stanford University)
Sang Michael Xie (Stanford University)
Benjamin Newman (Stanford University)
Percy Liang (Stanford University)
Percy Liang

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

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