Large-Model Content Compliance Detection With Word-Vector Clustering

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Solution Overview

Problem

Large models, with their complex computational structures and extensive training data, often generate inappropriate content due to unanalyzed patterns, leading to false or missed detections when directly comparing words, affecting user experience and safety.

Innovation Solution

A method involving preprocessing text generated by large models to obtain word vectors, clustering them based on importance, and determining compliance using word cluster centers and a sensitive word database to reduce false or missed detections.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If directly comparing words of the model-generated content, then the detection process is simple, but the structure feature of the content is ignored causing false or missed detections

Engineering Contradiction:

Improvedetection process complexityVSAvoiddetection accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent segments the text into individual words and converts them into word vectors, then clusters these vectors to identify structure features. This segmentation approach allows the system to analyze both individual words and their structural relationships, resolving the contradiction between simple detection and accurate structure feature recognition.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms words from one-dimensional text strings into multi-dimensional word vectors through embedding. This dimensionality change enables the capture of semantic relationships and structural features that are invisible in raw text comparison, thereby improving detection accuracy without excessive complexity.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Reliability

If analyzing the entire training data to improve model capabilities, then the model learns more patterns, but the computational cost and time increase significantly

Engineering Contradiction:

Improvemodel generalization capabilityVSAvoidtraining time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by pre-processing the training data into word vectors and clustering them before actual model training. This preliminary organization of data into structured representations accelerates the training process while maintaining the ability to learn complex patterns, thus reducing training time without sacrificing model capability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the parameter representation from raw text to clustered word vectors with specific dimensional features. This parameter transformation enables more efficient computation during training while preserving the semantic and structural information necessary for learning complex patterns and improving generalization capability.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12443643B1Methods, devices, and systems for extracting and analyzing content facing large model

Publication Date: 2025.10.14 INSTITUTE OF NETWORK TECHNOLOGY (YANTAI)

AI summary

A method, device, and system for extracting and analyzing content facing a large model are provided. The method comprises: by preprocessing a current text generated by the large model, obtaining a plurality of word vectors of the current text, the large model being a machine learning model; by clustering the plurality of word vectors according to an importance degree of the plurality of word vectors, obtaining a plurality of word cluster centers; and determining whether the current text is compliant based on the plurality of word cluster centers and a sensitive word database.