Conversational Agent Moderation with Lightweight Multi-Label Scoring
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Solution Overview
Problem
Conventional content moderation techniques for conversational agents, such as bad-word filters and reinforcement learning with human feedback, are inadequate in preventing abuse and misuse, and require significant computational resources or expose sensitive data to external APIs.
Innovation Solution
A lightweight Zero Shot Classifier (ZSC) combined with a Decision Tree (DT) for semantic moderation, allowing multi-label classification without softmax, operates on-premises and reduces the need for generalist LLMs or external APIs, leveraging a smaller dataset for supervised training and enhancing interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional content moderation techniques (bad-word filters, reinforcement learning with human feedback) are used, then content moderation capability is provided, but computational resources are significantly consumed and/or sensitive data is exposed to external APIs
Solution Approach 1:
The patent segments the content moderation task into multiple independent target classes, each scored separately without softmax normalization. This allows the system to evaluate different aspects of content (e.g., toxicity, harassment, spam) independently, reducing computational complexity while maintaining comprehensive moderation capability
Solution Approach 2:
The patent introduces an intermediary classification layer that sits between input reception and final moderation decisions. This intermediary system uses lightweight models to pre-process and categorize inputs, reducing the computational burden on heavier moderation components and enabling faster, more efficient content evaluation
2Reliability
If conventional content moderation techniques (bad-word filters, reinforcement learning with human feedback) are used, then content moderation capability is provided, but sensitive data is exposed to external APIs
Solution Approach 1:
The patent extracts the moderation capability from external API dependencies and implements it locally within the system. By using on-premises lightweight models for classification and scoring, the system maintains content moderation functionality while keeping sensitive data within the organization's infrastructure, eliminating the privacy risk associated with external API exposure
3Adaptability or versatility
If generalist LLMs are used for content moderation, then comprehensive language understanding is achieved, but computational costs and model size increase
Solution Approach 1:
The patent applies local quality by using specialized lightweight models tailored for specific moderation tasks rather than deploying a single generalist LLM. Each target class can be handled by optimized, domain-specific models that are smaller and more efficient, achieving comprehensive language understanding across multiple moderation categories without the overhead of a large general-purpose model
Solution Approach 2:
The patent creates a universal moderation framework that can handle multiple target classes and content types through a unified scoring mechanism. The system uses the same lightweight classification infrastructure across different moderation categories, achieving versatility without requiring separate large models for each function
Data Source
AI summary
Techniques are disclosed for semantic moderation of conversational agents. An example system includes a memory having instructions, and a processor communicatively coupled to the memory and configured to execute the instructions. Example instructions include: scoring a received input string against a plurality of target classes to derive two or more scores, wherein each target class is associated with an independent score, and the scoring is performed without applying a softmax function to the independent scores; using the scores to generate a rule-based determination that indicates whether to pass or filter the input string; upon a determination to filter the input string, performing filtering processing on the input string; and otherwise, performing output processing on the input string.