Foundation Model Fine-Tuning via Structured Data Generation
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Solution Overview
Problem
Professionals such as lawyers, consultants, and healthcare providers face challenges in generating natural language text descriptions due to time constraints and the need to switch between diverse contexts, while existing text generation tools often produce unreliable outputs with hallucinations.
Innovation Solution
A computer-implemented process that generates narratives by populating pre-generated template forms with UI elements, using a text-to-speech system, and inputting the populated forms into a trained machine learning model for fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical text generation is used in language models, then text generation speed and flexibility are improved, but reliability and accuracy deteriorate due to hallucinations
Solution Approach 1:
The patent introduces an intermediary verification mechanism between the statistical language model and the final output. A separate verification model checks the generated text against the original context, acting as a mediator to filter out hallucinations while preserving the speed benefits of statistical generation.
Solution Approach 2:
The system implements feedback loops where the generated text is continuously verified against the source context. The verification results feed back into the generation process, allowing the model to correct its own hallucinations and improve accuracy while maintaining generation speed.
2Reliability
If professionals manually write detailed narratives, then accuracy and context fidelity are improved, but time consumption and productivity deteriorate
Solution Approach 1:
Instead of requiring complete manual writing, the system performs partial action by automatically generating drafts of narratives based on available data. This partial automation handles the routine portions while professionals only need to review and refine, significantly reducing time consumption while maintaining accuracy.
Solution Approach 2:
The system performs preliminary action by pre-generating narrative drafts before professional review. This preliminary draft creation handles the initial composition work, allowing professionals to focus only on verification and refinement, thereby improving time efficiency without sacrificing narrative quality.
3Adaptability or versatility
If context switching is required for diverse tasks, then adaptability and versatility are improved, but cognitive load and error rate increase
Solution Approach 1:
The patent segments the context management into distinct modules: a context extraction module that captures relevant information, a context storage module that preserves it, and a context retrieval module that supplies it during generation. This segmentation allows the system to handle diverse tasks while maintaining context fidelity by systematically managing context transitions.
Data Source
AI summary
Provided is a process that includes receiving, via computing system, a template form comprising one or more unpopulated health information elements and a set of populated heath information elements; determining, with a generative language model, generated information based on the set of populated heath information elements of the template form, wherein the generated information relates to a first health information element of the one or more unpopulated health information elements; sending, with the computing system to a user computing device, a message prompting the user to accept the generated information; and responsive to receiving permission from the user computing device, storing the generated information in memory.