Where to start? Analyzing the potential value of intermediate models
Submission Type: Regular Long Paper
Submission Track: Machine Learning for NLP
Submission Track 2: Resources and Evaluation
Keywords: Intertraining, fine tuned, intermediate, finetune
TL;DR: Rank good models, not similar models
Abstract: Previous studies observed that finetuned models may be better base models than the vanilla pretrained model. Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset.
Here, we perform a systematic analysis of this \emph{intertraining} scheme, over a wide range of English classification tasks. Surprisingly, our analysis suggests that the potential intertraining gain can be analyzed \emph{independently} for the target dataset under consideration, and for a base model being considered as a starting point. Hence, a performant model is generally strong, even if its training data was not aligned with the target dataset. Furthermore, we leverage our analysis to propose a practical and efficient approach to determine if and how to select a base model in real-world settings. Last, we release an updating ranking of best models in the HuggingFace hub per architecture.
Submission Number: 516
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