Natural-Language Control of Content Generation With Self-Correction
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Solution Overview
Problem
Existing generative AI systems face challenges such as non-deterministic outputs, difficulty in formulating effective prompts, inconsistent content generation, and susceptibility to human and AI errors, particularly in multi-modal content creation and retrieval tasks.
Innovation Solution
A system utilizing a generative AI platform with dynamic prompt generation, self-correcting mechanisms, and multi-pass architectures to enhance content generation, summarization, and retrieval, incorporating features like callback functions and semantic search capabilities to improve consistency and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are used for content generation, then creativity and content production capability are improved, but output consistency and determinism deteriorate
Solution Approach 1:
The patent implements a self-correcting mechanism where the generative AI model's output is evaluated against constraints and requirements, and feedback is provided to adjust subsequent generations. This closed-loop feedback system improves output consistency while maintaining the model's creative capabilities.
Solution Approach 2:
The system dynamically adjusts generation parameters and constraints based on the specific task requirements and desired output characteristics. This allows the model to adapt its behavior to achieve consistent results across different content generation scenarios while preserving versatility.
2Manufacturing precision
If complex prompts are formulated to improve content quality, then content accuracy and relevance are improved, but system complexity and difficulty of operation worsen
Solution Approach 1:
The system automatically generates optimized prompts based on the task requirements and desired output characteristics, eliminating the need for users to manually craft complex prompts. This self-service approach maintains high content accuracy while significantly improving ease of operation.
Solution Approach 2:
The system performs preliminary analysis of task requirements and pre-generates appropriate prompts and constraints before the actual content generation process. This preliminary action ensures accurate and relevant content output without requiring users to understand complex prompt engineering.
3Reliability
If multiple passes and self-correction mechanisms are implemented, then content quality and consistency are improved, but computational load and processing time worsen
Solution Approach 1:
The system applies self-correction and multiple passes selectively based on the specific task requirements and desired quality levels. For tasks requiring high consistency, full multi-pass processing is applied, while for less critical tasks, simplified single-pass generation is used, optimizing computational resource utilization.
Solution Approach 2:
The content generation process is segmented into distinct phases (initial generation, evaluation, correction, finalization), allowing the system to apply computational resources efficiently at each stage and enable parallel processing where applicable, thereby managing overall computational load.
4Adaptability or versatility
If generative AI systems are used for multi-modal content creation, then content creation capability is improved, but susceptibility to errors worsens
Solution Approach 1:
The system implements cross-modal feedback mechanisms where errors and inconsistencies detected in one modality (e.g., text) trigger corrections in related modalities (e.g., image generation). This feedback loop reduces error propagation across multiple modalities while maintaining versatile content creation capabilities.
Solution Approach 2:
The system applies constraints and validation rules before content generation to prevent common errors, and performs preliminary checks on generated content to catch and correct issues early. This proactive error prevention and correction approach reduces overall error susceptibility in multi-modal content creation.
Data Source
AI summary
A system may include a content repository that stores multi-modal content comprising text, visual content, and/or audio content. The system may access a prompt comprising a text query that describes text to be found, a visual query comprising text that describes a visual to be found, and/or an audio query comprising text that describes audio to be found, execute a language model based on the prompt to identify content from the content repository, receive, from the language model, a request for a callback function that seeks additional information to satisfy the multi-modal query, execute the callback function to obtain the additional information and provide the additional information to the language model in response to the request for the callback function, re-execute the language model based on the multi-modal query and the additional information, and obtain, from the language model, content responsive to the prompt based on the additional information.