![]() ![]() In practice, this enables storing a single copy of a large model and many much smaller files with task-specific modifications. ![]() Consequently, recent work has focused on keeping most of the model parameters fixed and fine-tuning a small number of parameters per task. When a model needs to be fine-tuned in many settings such as for a large number of users, it is computationally expensive to store a copy of a fine-tuned model for every scenario. For instance, by fine-tuning a model on text where gendered words are replaced with those of the opposite gender, a model can be made more robust to gender bias ( Zhao et al., 2018 Zhao et al., 2019 Manela et al., 2021). A pre-trained model is trained with the pre-training loss (typically masked language modelling) on data that is closer to the target distribution.įormally, given a target domain $\mathcal$ as it allows us to encode the desired capabilities directly in the data. Adaptive fine-tuning as part of the standard transfer learning setting. Importantly, the model is fine-tuned with the pre-training objective, so adaptive fine-tuning only requires unlabelled data. Specifically, adaptive fine-tuning involves fine-tuning the model on additional data prior to task-specific fine-tuning, which can be seen below. Adaptive fine-tuning is a way to bridge such a shift in distribution by fine-tuning the model on data that is closer to the distribution of the target data. Adaptive fine-tuningĮven though pre-trained language models are more robust in terms of out-of-distribution generalisation than previous models ( Hendrycks et al., 2020), they are still poorly equipped to deal with data that is substantially different from the one they have been pre-trained on. Overview of fine-tuning methods discussed in this post. In particular, I will highlight the most recent advances that have shaped or are likely to change the way we fine-tune language models, which can be seen below. Consequently, fine-tuning is the main focus of this post. Fine-tuning is more important for the practical usage of such models as individual pre-trained models are downloaded-and fine-tuned-millions of times (see the Hugging Face models repository). While pre-training is compute-intensive, fine-tuning can be done comparatively inexpensively. The standard pre-training-fine-tuning setting (adapted from (Ruder et al., 2019)) The pre-trained model is then fine-tuned on labelled data of a downstream task using a standard cross-entropy loss. ![]() In the standard transfer learning setup (see below see this post for a general overview), a model is first pre-trained on large amounts of unlabelled data using a language modelling loss such as masked language modelling (MLM Devlin et al., 2019). The limitations of this zero-shot setting (see this section), however, make it likely that in order to achieve the best performance or stay reasonably efficient, fine-tuning will continue to be the modus operandi when using large pre-trained LMs in practice. Recent models are so large in fact that they can achieve reasonable performance without any parameter updates ( Brown et al., 2020). The empirical success of these methods has led to the development of ever larger models ( Devlin et al., 2019 Raffel et al., 2020). Over the last three years ( Ruder, 2018), fine-tuning ( Howard
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |