Introduction

Adaptation⚑

Adaptation in AI is a crucial step to enhance the capabilities of foundation models, allowing them to cater to specific tasks and domains. This process is about tailoring pre-trained AI systems with new data, ensuring they perform optimally in specialized applications and respect privacy constraints. Reaping the benefits of adaptation leads to AI models that are not only versatile but also more aligned with the unique needs of organizations and industries.

Adapting foundation models is essential due to their limitations in specific areas despite their extensive training on large datasets. Although they excel at many tasks, these models can sometimes misconstrue questions or lack up-to-date information, which highlights the need for fine-tuning. By addressing these weaknesses through additional training or other techniques, the performance of foundation models can be significantly improved.

• Prompting

• Probing: Using probing to train a classifier is a powerful approach to tailor generative AI foundation models, like BERT, for specific applications. By adding a modestly-sized neural network, known as a classification head, to a foundation model, one can specialize in particular tasks such as sentiment analysis. This technique involves freezing the original model's parameters and only adjusting the classification head through training with labeled data. Ultimately, this process simplifies adapting sophisticated AI systems to our needs, providing a practical tool for developing efficient and targeted machine learning solutions.

  • Linear Probing: A simple form of probing that involves attaching a linear classifier to a pre-trained model to adapt it to a new task without modifying the original model.

  • Classification Head: It is the part of a neural network that is tailored to classify input data into defined categories.

  • Transfer learning

  • Fine Tuning: Fine-tuning is an important phase in enhancing the abilities of generative AI models, making them adept at specific tasks. By introducing additional data to these powerful models, they can be tailored to meet particular requirements, which is invaluable in making AI more effective and efficient. Although this process comes with its challenges, such as the need for significant computational resources and data, the outcome is a more specialized and capable AI system that can bring value to a wide range of applications.

  • Fine tuning is a type of transfer learning.

  • Traditional fine tuning consist in update all the weigths of a training model. (Need more resource a much more data)

Resources⚑

• Create a sentiment classifier with Bert