Language Model Compliance Checking With Hallucination Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing compliance checking processes for content items, such as texts, images, and videos, are inefficient, labor-intensive, and prone to inaccuracies due to the limitations of deterministic logic and the potential for Large Language Models (LLMs) to hallucinate, making them unsuitable for large-scale and accurate compliance checks.
Innovation Solution
A system utilizing tailored prompts and validation techniques with LLMs to guide compliance checks, incorporating preprocessing, output processing, and user interface features to enhance accuracy and efficiency, reducing LLM hallucinations and improving turnaround time.
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 labor-intensive for large-scale content items
Solution Approach 1:
The patent replaces traditional deterministic mechanical compliance checking processes with Large Language Models (LLMs) that use probabilistic reasoning and natural language understanding. This substitution enables the system to process large volumes of content items rapidly while maintaining compliance accuracy, resolving the contradiction between checking efficiency and turnaround time.
2Productivity
If LLMs are used for compliance checking, then processing speed and scalability improve, but accuracy deteriorates due to hallucinations
Solution Approach 1:
The patent implements a feedback mechanism where the system generates compliance check results using LLMs, validates these results against ground truth data, and uses the validation outcomes to fine-tune and improve the LLM's performance. This continuous feedback loop maintains high processing throughput while progressively improving accuracy and reducing hallucinations.
Solution Approach 2:
The patent performs preliminary actions by generating synthetic ground truth data and validation datasets before deploying the LLM for production compliance checking. This preparatory work establishes accuracy benchmarks and enables the system to maintain reliable results while scaling processing capacity.
3Reliability
If manual compliance checking is performed, then accuracy can be maintained, but the process becomes labor-intensive and slow
Solution Approach 1:
The patent implements self-service by enabling the compliance checking system to automatically generate its own validation data, assess its performance, and fine-tune its models without requiring continuous manual intervention. This automation maintains high accuracy while dramatically reducing operational complexity and labor requirements compared to manual checking processes.
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.