What is fine-tuning and how does it improve model accuracy?
Asked on Sep 19, 2025
Answer
Fine-tuning is a process in AI where a pre-trained model is further trained on a specific dataset to improve its performance for a particular task. This approach leverages the model's existing knowledge and adapts it to new, task-specific data, enhancing accuracy and efficiency.
Example Concept: Fine-tuning involves taking a model that has already been trained on a large, general dataset and continuing its training on a smaller, task-specific dataset. This process adjusts the model's parameters slightly to better fit the new data, allowing it to perform more accurately on the specific task without starting from scratch. It is particularly useful in transfer learning, where the knowledge gained from one task is applied to improve performance on a related task.
Additional Comment:
- Fine-tuning is typically faster and requires less data than training a model from scratch.
- It helps in adapting models to domain-specific language or tasks, such as medical or legal text analysis.
- Careful selection of hyperparameters is crucial during fine-tuning to avoid overfitting.
- Fine-tuning can be applied to various types of models, including language models, image classifiers, and more.
Recommended Links: