LLM Incident Report Generation with Hallucination Gates
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Solution Overview
Problem
Current methods for generating incident reports, such as police reports and insurance reports, are time-consuming and inefficient, despite advancements like auto-completion and OCR technology.
Innovation Solution
The use of large language models (LLMs) integrated with semantic search agents and hallucination gates to facilitate the generation and refinement of incident reports, allowing for real-time feedback and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual report generation methods are used, then officers can ensure accuracy and control over report content, but the time and effort required to create reports increases significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of LLMs, semantic search agents, and hallucination gates that acts as a mediator between raw incident data and final report generation. This intermediary automatically processes and structures information, reducing the time officers spend on manual report writing while maintaining accuracy through multiple verification layers including semantic search validation and hallucination detection mechanisms
Solution Approach 2:
The patent implements feedback loops where the LLM-generated reports are evaluated by hallucination gates and semantic search agents that provide feedback on accuracy and consistency. Officers can also provide feedback through the graphical user interface to refine and correct reports, ensuring high accuracy while significantly reducing the time required compared to traditional manual methods
2Ease of operation
If advanced technologies like OCR and auto-completion are used, then some assistance is provided to officers, but the solutions remain inadequate and do not sufficiently reduce the burden
Solution Approach 1:
The patent merges multiple technological components including LLMs for natural language generation, semantic search agents for information retrieval and validation, hallucination gates for accuracy verification, and OCR for data extraction. This integrated system provides comprehensive assistance that goes beyond isolated tools like auto-completion or OCR, significantly improving both ease of operation and overall productivity by handling multiple aspects of report generation simultaneously
Solution Approach 2:
The LLM-based system performs multiple functions including extracting information from incident data, generating narrative reports, validating consistency through semantic search, detecting hallucinations, and allowing iterative refinement. This multi-functional approach replaces the need for multiple separate tools and manual processes, dramatically improving productivity while maintaining ease of use through a unified interface
3Loss of time
If LLMs are used for automatic report generation, then time and effort are reduced, but concerns about accuracy and hallucination may arise
Solution Approach 1:
The patent implements preliminary actions by training the LLM on domain-specific incident report data and pre-configuring semantic search agents with relevant knowledge bases before report generation begins. This preliminary preparation ensures the LLM has accurate information and appropriate context, reducing the likelihood of hallucinations and improving reliability from the outset while maintaining fast generation speeds
Solution Approach 2:
The patent employs beforehand cushioning through multiple validation mechanisms including semantic search agents that verify information consistency, hallucination gates that detect and flag potential inaccuracies, and iterative refinement processes that allow correction of errors. These protective measures are built into the system architecture to cushion against potential accuracy issues while preserving the speed benefits of automated generation
Data Source
AI summary
The present disclosure relates to utilizing large language models (LLMs) to facilitate generation of incident reports or similar documents. One or more initial inputs may be received from a user, and one or more example incident reports may be identified. The one or more example incident reports and the one or more initial inputs may be sent to an LLM. A reviewable version of an incident report may be accessed that is based on output that the LLM generated based on the example incident reports and the one or more initial inputs. The reviewable version of the incident report may be presented in a human readable format via a graphical user interface (GUI). A modification corresponding to the reviewable version of the incident report may be received via the GUI. The modification and the reviewable version of the incident report may be sent to the LLM to cause the LLM to generate an updated version of the incident report.