Fine-tuning (deep learning)

In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data.^[1] 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" (i.e., not changed during backpropagation).^[2] 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.^[3]

For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as 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.^[2][4]

Models that are pre-trained on large, general corpora are usually fine-tuned by reusing their parameters as a starting point and adding a task-specific layer trained from scratch.^[5] Fine-tuning the full model is also common and often yields better results, but is more computationally expensive.^[6]

Fine-tuning is typically accomplished via supervised learning, but there are also techniques to fine-tune a model using weak supervision.^[7] Fine-tuning can be combined with a reinforcement learning from human feedback-based objective to produce language models such as ChatGPT (a fine-tuned version of GPT models) and Sparrow.^[8][9]

Robustness

Fine-tuning can degrade a model's robustness to distribution shifts.^[10][11] 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.^[12]

Variants

Low-rank adaptation

Low-rank adaptation (LoRA) is an adapter-based technique for efficiently fine-tuning models. The basic idea is to design a low-rank matrix that is then added to the original matrix.^[13] An adapter, in this context, is a collection of low-rank matrices which, when added to a base model, produces a fine-tuned model. It allows for performance that approaches full-model fine-tuning with lower space requirements. 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.^[14] Support for LoRA was integrated into the Diffusers library from Hugging Face.^[15] 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.^[16]

Representation fine-tuning

Representation fine-tuning (ReFT) is a technique developed by researchers at Stanford University aimed at fine-tuning large language models (LLMs) by modifying less than 1% of their representations. Unlike traditional parameter-efficient fine-tuning (PEFT) methods, which mainly focus on updating weights, ReFT targets specific parts of the model relevant to the task being fine-tuned. This approach is based on the understanding that deep learning models encode rich semantic information in their representations, suggesting that modifying representations might be a more effective strategy than updating weights.^[17]

ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations and train interventions that manipulate a small fraction of model representations to steer model behaviors towards solving downstream tasks at inference time. One specific method within the ReFT family is Low-rank Linear Subspace ReFT (LoReFT), which intervenes on hidden representations in the linear subspace spanned by a low-rank projection matrix.^[17] LoReFT can be seen as the representation-based equivalent of Low-rank Adaptation (LoRA).

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.^[6]

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.^[18][19][20]

See also

• Catastrophic forgetting
• Continual learning
• Domain adaptation
• Foundation model
• Hyperparameter optimization
• Overfitting