LLM Compliance Verification With Prompt Validation for Content Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing compliance checking processes for content items, such as texts, images, and videos, are inefficient, time-consuming, and prone to inaccuracies due to the limitations of deterministic logic and the hallucinations of Large Language Models (LLMs).
Innovation Solution
A content compliance check system utilizing tailored prompts and validation techniques to guide LLMs to generate accurate compliance results, incorporating preprocessing, output processing, and user interface enhancements to ensure efficient and precise compliance checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deterministic logic is used for compliance checking, then the process is straightforward and controllable, but it is inefficient and time-consuming
Solution Approach 1:
The patent replaces traditional deterministic mechanical checking processes with Large Language Models (LLMs) that use probabilistic reasoning and natural language understanding. This substitution enables parallel processing of multiple content items, dramatically improving compliance checking efficiency while reducing turnaround time from days to minutes or seconds.
Solution Approach 2:
The patent introduces an intermediary layer between content items and compliance rules that uses LLMs to interpret and apply rules flexibly. This intermediary enables rapid processing by pre-processing content items, extracting relevant features, and matching them against compliance rules in an optimized manner that reduces overall processing time.
2Productivity
If LLMs are used for compliance checking, then processing speed increases, but hallucinations and inaccuracies occur
Solution Approach 1:
The patent implements feedback mechanisms where LLM-generated compliance results are validated against ground truth data, compliance rules, and expert annotations. This feedback loop enables continuous refinement of model outputs, reducing hallucinations and improving accuracy while maintaining high processing speeds through iterative correction rather than reprocessing.
Solution Approach 2:
The patent performs preliminary actions by pre-processing content items before LLM analysis, extracting and structuring relevant features in advance. This preliminary preparation reduces the complexity of LLM processing, enabling faster and more accurate compliance checking by providing the LLM with pre-organized information that minimizes hallucination risks.
3Reliability
If manual compliance checking is performed, then accuracy can be maintained, but the process is too onerous and slow for large volumes of content
Solution Approach 1:
The patent segments the compliance checking process into distinct components: content pre-processing, LLM-based analysis, rule matching, and result validation. This segmentation enables parallel processing of multiple content items at different stages, dramatically increasing throughput while maintaining accuracy through specialized handling of each segment with appropriate validation checks.
Solution Approach 2:
The patent creates a universal compliance checking system that handles multiple content types (text, images, video, audio) and various compliance rules through a single LLM-based platform. This multi-functional approach maintains high accuracy by applying consistent validation principles across different content types while achieving high throughput through standardized processing pipelines.
Data Source
AI summary
Computer-implemented systems and methods are disclosed, including systems and methods for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.