Business Context
EditAI, a startup specializing in AI-assisted content creation, aims to enhance its text generation capabilities by implementing a reverse diffusion model. The goal is to generate coherent texts that are conditioned on specific user editing prompts, improving user engagement and satisfaction. The model should effectively handle diverse prompts and produce high-quality outputs.
Dataset
| Feature Group | Count | Examples |
|---|
| Text Data | 50K | User prompts, generated texts, reference texts |
| Metadata | 5 | Prompt length, user preferences, timestamp |
- Size: 50,000 user prompts with corresponding generated texts
- Target: Text generated based on user prompts
- Class balance: Balanced dataset with diverse prompt types
- Missing data: Minimal missing data, <2% in user preferences
Requirements
- Develop a reverse diffusion model to generate text conditioned on user prompts.
- Implement feature engineering strategies to enhance model performance.
- Evaluate the model using appropriate metrics, ensuring quality and relevance of generated texts.
- Provide a comprehensive explanation of the mathematical formulation of the reverse diffusion process and modifications for conditioning.
Constraints
- The model must generate responses in under 2 seconds per prompt.
- Outputs should maintain coherence and relevance to the provided prompts.
- The solution must be scalable to handle thousands of user requests simultaneously.
