Model Fine-Tuning

A Model Fine-Tuning Task is a Machine Learning Task that involves adjusting the parameters of a pre-trained Machine Learning Model to enhance its performance on a specific task or dataset.

• Context:
  • It can (typically) be applied to Deep Learning models, such as Large Language Models and Convolutional Neural Networks, that have been pre-trained on a large, generic dataset.
  • It can (often) involve a smaller, task-specific dataset for the fine-tuning process, where the model is adjusted to perform better on tasks such as Text Classification, Image Recognition, or Speech Recognition.
  • It can leverage the knowledge the model has gained during its initial pre-training phase, allowing for more efficient learning and better performance on the specific task with less data.
  • It can include techniques like Transfer Learning, where the learned features of a model trained on one task are applied to a different but related task.
  • It can (often) require careful selection of learning rates and other hyperparameters to prevent overfitting on the fine-tuning dataset while retaining the knowledge from the pre-training phase.
  • It can result in significantly improved model performance, especially in domains where collecting large amounts of labeled data is difficult or expensive.
  • ...
• Example(s):
  • Fine-tuning a pre-trained BERT model on a dataset of customer reviews to improve its performance on sentiment analysis tasks.
  • Adjusting a pre-trained ResNet model on a new dataset of medical images to enhance its accuracy in identifying specific diseases.
  • ...
• Counter-Example(s):
  • A Zero-Shot Learning Task where a model makes predictions on tasks it has not been explicitly trained on.
  • A Model Pre-Training Task where a model is initially trained on a large, general dataset to learn a broad representation of its input space.
• See: Model Instruction-Tuning, Machine Learning Task, Deep Learning, Transfer Learning, Overfitting, Learning Rate.

References

2018

• (Howard & Ruder, 2018) ⇒ J Howard, and S Ruder. (2018). “Universal language model fine-tuning for text classification.” In: arXiv preprint arXiv:1801.06146. [1]
  • NOTE: It compares the fine-tuning behavior of the ULMFiT model with traditional full model fine-tuning, highlighting significant improvements in validation error across various datasets.

2020

• (Gunel et al., 2020) ⇒ B Gunel, J Du, A Conneau, and V Stoyanov. (2020). “Supervised contrastive learning for pre-trained language model fine-tuning.” In: arXiv preprint arXiv:2011.01403. [2]
  • NOTE: It focuses on fine-tuning pre-trained language models using supervised contrastive learning for single sentence and sentence-pair classification tasks, enhancing model performance.

2017

• (Wang et al., 2017) ⇒ YX Wang, D Ramanan, and others. (2017). “Growing a brain: Fine-tuning by increasing model capacity.” In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [3]
  • NOTE: It investigates the impact of increasing model capacity during fine-tuning, showing that larger models allow for more natural adaptation and improved knowledge transfer.

2023

• (Fu et al., 2023) ⇒ Z Fu, H Yang, AMC So, W Lam, L Bing, and others. (2023). “On the effectiveness of parameter-efficient fine-tuning.” In: Proceedings of the AAAI Conference on Artificial Intelligence. [4]
  • NOTE: It evaluates the efficiency of fine-tuning pre-trained models, discussing the inefficiency of tuning the entire model for each task and proposing more parameter-efficient methods.

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