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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 training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
For AI systems based on pre-existing models, the focus is more on fine-tuning. Transfer learning allows engineers to take a model that has already been trained on a broad dataset and adapt it for a specific task using a smaller, task-specific dataset. This method dramatically reduces the complexity of the design and training phase.
The first step in its training is supervised fine-tuning (SFT). This step does not require the reward model. This step does not require the reward model. Instead, the pre-trained model is trained on a dataset D S F T {\displaystyle D_{SFT}} that contains prompt-response pairs ( x , y ) {\displaystyle (x,y)} .
The Stanford Institute for Human-Centered Artificial Intelligence's (HAI) Center for Research on Foundation Models (CRFM) coined the term "foundation model" in August 2021 [16] to mean "any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks". [17]
Transformers typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset. The pretrain dataset is typically an unlabeled large corpus, such as The Pile. Tasks for pretraining and fine-tuning commonly include: language modeling [12] next-sentence ...
AI alignment involves ensuring that an AI system's objectives match those of its designers or users, or match widely shared values, objective ethical standards, or the intentions its designers would have if they were more informed and enlightened. [40] AI alignment is an open problem for modern AI systems [41] [42] and is a research field ...
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [1] [2] and training cost.