Computer Science > Computation and Language
[Submitted on 2 May 2023 (v1), last revised 5 Jul 2023 (this version, v3)]
Title:Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models
View PDFAbstract:Fine-tuning large models is highly effective, however, inference can be expensive and produces carbon emissions. Knowledge distillation has been shown to be a practical solution to reduce inference costs, but the distillation process itself requires significant computational resources. Rather than buying or renting GPUs to fine-tune, then distill a large model, an NLP practitioner might instead choose to allocate the available budget to hire annotators and manually label additional fine-tuning data. In this paper, we investigate how to most efficiently use a fixed budget to build a compact model. Through extensive experiments on six diverse tasks, we show that distilling from T5-XXL (11B) to T5-Small (60M) is almost always a cost-efficient strategy compared to annotating more data to directly train a compact model (T5-Small). We further investigate how the optimal budget allocated towards computation varies across scenarios. We will make our code, datasets, annotation cost estimates, and baseline models available as a benchmark to support further work on cost-efficient training of compact models.
Submission history
From: Junmo Kang [view email][v1] Tue, 2 May 2023 17:56:16 UTC (9,518 KB)
[v2] Wed, 3 May 2023 00:36:38 UTC (9,517 KB)
[v3] Wed, 5 Jul 2023 20:41:59 UTC (9,542 KB)
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