Text-Based Markup Conversion for Context-Rich LLM Extraction
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Solution Overview
Problem
Existing systems lack an efficient method to provide context for large language models using text-based document formats, which limits their ability to effectively extract information from electronic documents.
Innovation Solution
A system and method that convert electronic source documents into mark-up documents in a text-based format, which are then used to fine-tune a large language model and enable user-directed extraction of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electronic documents are processed using existing extraction methods, then information can be extracted, but the accuracy and relevance of extracted information is limited due to lack of contextual understanding
Solution Approach 1:
The patent introduces a text-based document format (XML/HTML markup) as an intermediary representation between the original electronic document and the large language model. This intermediary format preserves structural and contextual information that is lost in traditional extraction methods, enabling the LLM to understand document hierarchy, relationships, and semantics while improving extraction accuracy.
Solution Approach 2:
The system performs preliminary conversion of electronic documents into text-based markup formats before processing by the large language model. This pre-processing step encodes contextual information in a structured manner, preparing the data in advance to enhance the model's understanding and subsequent extraction accuracy without losing important contextual relationships.
2Adaptability or versatility
If traditional information extraction methods are used, then processing can be performed, but the system lacks the ability to understand contextual relationships in documents
Solution Approach 1:
The patent employs a universal text-based document format (XML/HTML) that can represent various types of electronic documents while preserving their structural and contextual characteristics. This universal format enables the large language model to process diverse document types with contextual understanding, achieving adaptability without requiring separate processing systems for each document type.
Solution Approach 2:
The system creates a textual copy of the electronic document in markup format that replicates the document's structure and content. This copy serves as a faithful representation that can be processed by the LLM to extract contextual relationships, maintaining the original document's information while enabling advanced processing capabilities.
3Measurement precision
If markup conversion and fine-tuning are implemented, then information extraction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The conversion to text-based markup format is performed as a preliminary step before LLM processing. By preparing the document in advance with proper structural encoding, the system enables the LLM to focus on contextual understanding and extraction during inference, potentially reducing the time required for the most computationally intensive part of the process while maintaining high accuracy.
Data Source
AI summary
Systems and methods for using a text-based document format to provide context for a large language model are disclosed. Exemplary implementations may: obtain an electronic source document in an electronic format; convert the electronic source document to a mark-up document in the text-based document format; obtain a large language model; fine-tune the large language model through a set of documents that are encoded in the text-based document format; provide the mark-up document to the large language model as input for user-directed extraction of information; present a user interface to a user to enable the user to control the user-directed extraction of the information from the mark-up document; and/or other steps.