Text Classification Marking Using Semantic Rule Retrieval
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Solution Overview
Problem
Existing techniques for marking classification of textual data are tedious, time-consuming, and plagued with inaccuracies and inconsistencies, often requiring human interaction and fine-tuning of large language models (LLMs) to implement different use cases.
Innovation Solution
A system utilizing a processor interfaced with a database and LLM, performing semantic searches and curating operations to autonomously classify textual data, integrating Retrieval-Augmented Generation (RAG) to manage interactions with the LLM, allowing for classification without fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used for marking classification of textual data, then human interaction and fine-tuning are required, but the process becomes tedious, time-consuming, and plagued with inaccuracies
Solution Approach 1:
The system performs self-service by automatically selecting and applying classification rules to textual data without requiring human interaction or manual fine-tuning. The processor autonomously executes the classification process, eliminating the need for human operators to manually adjust parameters or review each classification decision, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system implements preliminary action by pre-storing classification rules in a database before the actual classification process. This allows the processor to quickly retrieve and apply relevant rules during classification, eliminating the need for real-time human intervention or on-the-spot fine-tuning, thus reducing time consumption while maintaining accuracy.
2Adaptability or versatility
If existing techniques are used to implement various use cases, then changes to core architecture are required, but this reduces system adaptability
Solution Approach 1:
The system segments the classification functionality into independent classification rules stored in a database, separate from the core processor architecture. This allows different use cases to be implemented by simply adding or modifying rules in the database without changing the core architecture, thereby increasing adaptability while maintaining architectural simplicity.
Solution Approach 2:
The processor is designed with universal functionality to handle multiple use cases through a single classification mechanism. By using a unified approach where the processor retrieves and applies classification rules from a database, the system can accommodate various use cases without requiring separate architectures or complex modifications, thus achieving versatility without increasing device complexity.
3Measurement precision
If LLM fine-tuning is performed for different use cases, then classification accuracy improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The system extracts the classification knowledge from the LLM fine-tuning process and stores it as explicit classification rules in a database. Instead of embedding domain-specific knowledge through complex fine-tuning, the system extracts and formalizes the classification logic into reusable rules that can be directly applied, reducing model complexity while maintaining accuracy.
Solution Approach 2:
The system uses copying by retrieving pre-defined classification rules from the database rather than generating them through complex fine-tuning processes. This allows the system to replicate proven classification logic across different use cases without repeating the expensive and complex fine-tuning process, thereby maintaining accuracy while reducing complexity.
4Reliability
If manual classification marking is performed, then detailed control is achieved, but productivity decreases
Solution Approach 1:
The processor performs self-service by automatically retrieving classification rules from the database and applying them to textual data without human intervention. This automated self-service process maintains reliable control over classification decisions while dramatically increasing productivity by eliminating manual marking operations.
Solution Approach 2:
The system implements feedback by continuously retrieving and applying classification rules from the database, ensuring that each classification decision is based on the most current and relevant rules. This feedback mechanism maintains reliable control over the classification process while enabling high-speed automated processing, thus improving productivity without sacrificing control.
Data Source
AI summary
Embodiments can relate to systems and methods for autonomously marking classification of textual data. Textual data and a classification guide can be submitted to an interfacing module configured to interface a processor with a database and a large language model. A semantic search of textual data against classification rules that fall within the classification guide can be done to identify a first subset of classification rules. A curating operation of the textual data against the first subset of classification rules can be done to generate a second subset of classification rules to be used to classify the textual data. A prompt can be generated including the second subset of classification rules and instructions for a response. The prompt and textual data can be sent to the LLM to generate the response as an output document autonomously modified to include a classification marking.