LLM Field Object Generation for Accurate Document Autofill
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Solution Overview
Problem
Existing automated digital form filling systems suffer from inaccuracies due to generalized machine learning models that hallucinate and require excessive user interactions, leading to inefficiencies and resource wastage.
Innovation Solution
A field object generation system utilizing a large language model to determine and combine relevant data from source content items with fillable digital documents, employing summarization and aggregation validation to improve accuracy and reduce user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized machine learning models are used for digital form filling, then broad coverage of output generation is achieved, but accuracy deteriorates due to hallucinations
Solution Approach 1:
The system segments the form filling task into multiple independent steps: retrieving relevant information from source documents, generating field objects with proper data types, validating the generated content, and then populating the form fields. This segmentation allows each step to be optimized independently, improving overall accuracy while maintaining versatility.
Solution Approach 2:
The system implements feedback mechanisms where generated field objects are validated against the source documents and form requirements before being used. This feedback loop prevents hallucinations by verifying that generated content accurately reflects the source information and meets the required data types and formats.
2Productivity
If multiple interfaces and applications are used for data input, then comprehensive data processing is achieved, but navigational efficiency deteriorates
Solution Approach 1:
The system merges multiple data processing functions into a single integrated interface. It combines source document retrieval, information extraction, field object generation, and form population in one unified system, eliminating the need to switch between multiple applications and interfaces while maintaining comprehensive processing capability.
Solution Approach 2:
The system implements a universal interface that performs multiple functions: accessing source documents, analyzing content, generating appropriate field objects with type validation, and populating form fields. This multi-functional approach consolidates what would traditionally require separate tools into one system.
3Adaptability or versatility
If multiple applications run simultaneously for data input, then comprehensive functionality is achieved, but computer resource consumption increases
Solution Approach 1:
The system combines multiple applications into a single integrated solution, consolidating resource usage. By merging document retrieval, analysis, field object generation, and form population functions into one system, it eliminates the overhead of running multiple separate applications while maintaining all necessary functionalities.
4Measurement precision
If excessive user interactions are required for form filling, then precise data input is achieved, but time consumption increases
Solution Approach 1:
The system performs self-service by automatically retrieving source documents, extracting relevant information, generating appropriate field objects with correct data types, and populating form fields without requiring extensive user intervention. This automation maintains precision while dramatically reducing the time users spend interacting with the system.
Solution Approach 2:
The system performs preliminary actions by pre-retrieving and analyzing source documents before the user needs to fill out the form. It prepares field objects with validated data types in advance, so when the user initiates form filling, the data is already ready and verified, eliminating the need for manual data entry and verification steps.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for modifying a fillable digital document. In particular, the disclosed systems can receive a user interaction requesting to populate one or more aggregated data fields in a fillable digital document. In response to the request, the field object generation system can determine the data relevant to one or more aggregated data fields in the fillable digital document by utilizing a large language model to process one or more source content items for a user account. Further the systems and generate a field object from the data relevant to one or more aggregated data field and modify the fillable digital document by including the field object in the fillable digital document.