LLM Report Generation from Structured Data with Prior-Report Links
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating reports are time-consuming and difficult for journalists, lacking efficiency and accuracy in preserving factual information.
Innovation Solution
An apparatus and method utilizing a processor and memory to receive structured data, determine activity classification, and generate a final report using a large language model (LLM) that includes identifying and inserting report links based on previous reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current manual methods are used for generating reports, then journalists can write articles with human judgment, but the process is time-consuming and inefficient
Solution Approach 1:
The report generation process is divided into distinct segments: structured data reception, activity classification determination, structured data report generation, and final report generation using LLM. This segmentation allows each component to be optimized independently, improving overall efficiency while maintaining quality control at each stage.
Solution Approach 2:
A structured data report serves as an intermediary between the raw structured data and the final natural language report. This intermediate representation preserves factual accuracy while enabling efficient processing, as the LLM only needs to process the structured report rather than raw data, significantly reducing generation time.
2Productivity
If automated methods are used for generating reports, then efficiency is improved, but factual accuracy may be compromised
Solution Approach 1:
Activity classification is determined in advance before final report generation. This preliminary classification organizes the structured data into meaningful categories, ensuring that the LLM receives pre-processed, factually organized information. This reduces the risk of factual errors while maintaining high generation efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms where the structured data report is generated and reviewed before final LLM processing. This multi-stage verification ensures factual accuracy is maintained throughout the automated generation process, allowing efficient automation without compromising reliability.
3Loss of information
If report links are inserted based on previous reports, then report coherence and context are improved, but the complexity of the generation process increases
Solution Approach 1:
The system identifies and copies relevant report tags from previous reports to create contextual links. Rather than analyzing entire previous reports, it extracts and reuses specific tag information, maintaining contextual coherence while minimizing the complexity increase. This selective copying approach preserves important information without requiring complex processing of historical data.
Data Source
AI summary
An apparatus and method for generating a report, the apparatus including at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate reports from structured data using large language models.