Neural Network Message Generation with Feedback-Driven Truthfulness
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Solution Overview
Problem
Current artificial intelligence systems for generating linguistically complex messages, particularly for initiating communications like 'cold call' email campaigns, are limited in their ability to provide effective and personalized content, often lacking reliability and truthfulness, and fail to efficiently assist organizations in achieving sales or organizational goals.
Innovation Solution
A system utilizing multiple neural networks trained on extensive natural language data, including initial training sets and situational prompts, generates draft messages by evaluating and prioritizing instructional prompts, ensuring semantic element incorporation and message context, with a sampling engine selecting elements to create linguistically complex and effective communications, while continuously learning and improving through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are used to generate linguistically complex messages, then message generation capability is improved, but reliability and truthfulness deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where users can rate and provide feedback on generated messages. This feedback is used to continuously refine and retrain the neural networks, improving truthfulness and reliability over time while maintaining message generation capability
Solution Approach 2:
The system performs preliminary actions by pre-training neural networks on extensive natural language data and establishing ground truth datasets before actual message generation. This preparation ensures that the models start with a foundation of accurate language understanding and truthfulness
2Productivity
If neural networks generate messages automatically, then productivity is improved, but message quality and personalization deteriorate
Solution Approach 1:
The system segments the message generation process into multiple stages: initial neural network generation for efficiency, followed by separate evaluation and refinement stages that assess quality and personalization. This segmentation allows each stage to optimize for its specific function while maintaining overall productivity
Solution Approach 2:
The system applies local quality improvements by allowing users to selectively edit and refine specific portions of generated messages rather than requiring complete regeneration. This enables maintenance of high productivity while improving local message quality and personalization where needed
3Extent of automation
If existing AI systems are used for message generation, then automation is improved, but adaptability to complex communication goals deteriorates
Solution Approach 1:
The system implements dynamics by making the neural network models adaptable through continuous learning from user feedback and varying communication contexts. This allows the automated system to adjust its behavior and message generation strategies to match specific communication goals and organizational contexts
Data Source
AI summary
Provided are methods and systems for automated or semi-automated generation of complex messages. Provided systems include neural network(s) that are trained with at least an initial training set including message records having specific characteristics, such as size and form characteristics, and recognize certain user inputted content as “instructional prompts.” The neural network(s) use the instructional prompts, training set, and other prompts to generate a distribution of semantic element options for each semantic element the system determines to include in system drafted message(s). The system selects from among such options to generate a plurality of draft messages which are presented to users for evaluation, editing, or transmission, with the instructional prompts treated as priority content. The systems and methods include mechanisms for reviewing and changing the instructional prompts based on factors that can include the content of the system-generated draft messages before further iterations to enhance the accuracy of future messages.