Session-Concept Prompt Personalization for Generative Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for prompt suggestion in generative models do not consider the unifying concept of a user session, leading to suggestions that are unrelated to the user's intent, resulting in generated content that is not personalized.
Innovation Solution
A system that extracts a session concept from a sequence of prompts and uses a prompt generation model to suggest personalized prompts anchored in that concept, utilizing machine learning models like transformers and diffusion models to generate relevant suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing prompt suggestion systems are used without session concept extraction, then the system complexity is reduced, but the relevance and personalization of prompt suggestions deteriorate
Solution Approach 1:
The system segments the prompt generation process into distinct components: a concept generation model that extracts session concepts from prompt sequences, and a prompt generation model that uses these concepts to suggest personalized prompts. This segmentation allows each model to specialize in a specific task, improving overall personalization while managing complexity through modular architecture.
Solution Approach 2:
Session concepts serve as an intermediary between the user's prompt sequence and the personalized prompt suggestions. The concept generation model extracts these intermediate concepts, which then guide the prompt generation model to produce relevant suggestions. This intermediary mechanism enables personalization without requiring direct complex analysis of entire prompt histories.
2Productivity
If prompt suggestions are generated without anchoring in session concepts, then the generation speed is improved, but the coherence and relevance of generated content deteriorates
Solution Approach 1:
The system performs preliminary action by extracting session concepts from the prompt sequence before generating personalized prompt suggestions. This pre-extraction of key concepts allows the prompt generation model to quickly produce relevant suggestions without analyzing the entire prompt history each time, thus maintaining generation speed while ensuring coherence through concept anchoring.
3Adaptability or versatility
If a concept generation model is introduced to extract session concepts, then the personalization of suggestions is improved, but the training data requirements and model complexity increase
Solution Approach 1:
The concept generation model is designed with multi-functionality, serving both to extract session concepts for personalization and to provide training data for the prompt generation model. By synthesizing training data that includes concept-extraction pairs from prompt sequences, the system achieves multiple objectives with a single model component, reducing overall complexity despite the added capability.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for generating suggested prompts include obtaining a sequence of text prompts associated with a user and determining a session concept for the user based on the sequence of text prompts. Embodiments then generate, using a prompt generation model, an image generation prompt based on the sequence of text prompts and the session concept. Subsequently, embodiments generate, using an image generation model, a synthetic image based on the image generation prompt.