Fine-tuning (machine Learning)

In deep learning, fine-tuning is an approach to transfer learning in which the weights of a pre-trained model are trained on new data. Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (not updated during the backpropagation step). A model may also be augmented with "adapters" that consist of far fewer parameters than the original model, and fine-tuned in a parameter-efficient way by tuning the weights of the adapters and leaving the rest of the model's weights frozen.

For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen because they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model is trained on.

Models that are pre-trained on large and general corpora are usually fine-tuned by reusing the model's parameters as a starting point and adding a task-specific layer trained from scratch. Fine-tuning the full model is common as well and often yields better results, but it is more computationally expensive.

Fine-tuning is typically accomplished with supervised learning, but there are also techniques to fine-tune a model using weak supervision. Fine-tuning can be combined with a reinforcement learning from human feedback-based objective to produce language models like ChatGPT (a fine-tuned version of GPT-3) and Sparrow.

Robustness

Fine-tuning can degrade a model's robustness to distribution shifts. One mitigation is to linearly interpolate a fine-tuned model's weights with the weights of the original model, which can greatly increase out-of-distribution performance while largely retaining the in-distribution performance of the fine-tuned model.

Variants

Low-rank adaptation

Low-rank adaptation (LoRA) is an adapter-based technique for efficiently finetuning models. The basic idea is to design a low- rank matrix that is then added to the original matrix. An "adapter" in this context is a collection of low-rank matrices, which when added to a base model, produces a finetuned model. It allows for performance that approaches full-model fine-tuning with less space requirement. A language model with billions of parameters may be LoRA fine-tuned with only several millions of parameters.

LoRA-based fine-tuning has become popular in the Stable Diffusion community. Support for LoRA is being integrated into the Diffusers library from Hugging Face. Support for LoRA and similar techniques is also available for a wide range of other models through Hugging Face's Parameter-Efficient Fine-Tuning (PEFT) package.

Applications

Natural language processing

Fine-tuning is common in natural language processing (NLP), especially in the domain of language modeling. Large language models like OpenAI's series of GPT foundation models can be fine-tuned on data for specific downstream NLP tasks (tasks that use a pre-trained model) to improve performance over the unmodified pre-trained model.

Commercial models

Commercially-offered large language models can sometimes be fine-tuned if the provider offers a fine-tuning API. As of June 19, 2023, language model fine-tuning APIs are offered by OpenAI and Microsoft Azure's Azure OpenAI Service for a subset of their models, as well as by Google Cloud Platform for some of their PaLM models, and by others. Not all commercial models currently support fine-tuning.

See also

* Domain adaptation
* Large language models
* Transfer learning

References

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Machine learning
Artificial intelligence