1. Fine-tuning: GPT can be fine-tuned for specific tasks such as language modeling, sentiment analysis, text classification, summarization, and question answering. This involves training the model on a specific dataset that is relevant to that task.

2. Generative content creation: GPT can be used to generate human-like content such as writing articles, composing emails, or even writing music. This is achieved by providing a prompt to the model which it uses to generate new content.

3. Text completion: GPT can be used to complete sentences or paragraphs in a way that is contextually sensitive. This is especially useful for tasks such as chatbots, where text completion is an important part of the conversation.

4. Multi-task learning: GPT can be trained on multiple tasks simultaneously, allowing the model to learn to perform multiple tasks at once. This is achieved by introducing additional input features for each task and training the model on a combined dataset.

5. Transfer learning: GPT can be used as a pre-training step for other models, allowing them to transfer the knowledge learned by GPT to their own specific tasks. This can greatly improve the performance of other models in areas such as text classification or sentiment analysis.

6. Style transfer: GPT can be used to transfer the writing style of one piece of content to another. This is achieved by fine-tuning the model on a specific writing style, and then generating content based on a piece of input text.

7. Dialogue generation: GPT can be used to generate human-like dialogue between two or more people. This is achieved by training the model on a dataset of conversational dialogues and then using it to generate new dialogues based on a given context.

8. Image captioning: GPT can be used to generate captions for images, allowing for more accurate descriptions of the content in the image. This is achieved by training the model on a dataset of image-caption pairs and then using it to generate captions for new images.

9. Story generation: GPT can be used to generate complete stories, including characters, plot, and dialogue. This is achieved by training the model on a dataset of stories and then using it to generate new stories based on a given prompt.