Context-Aware Prompt Filtering for Domain-Specific AI Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing approaches to large language model (LLM) safety filtering struggle to accurately distinguish between legitimate inquiries and harmful content, particularly in sensitive domains like medicine, leading to potential misinformation and resource inefficiency.
Innovation Solution
A context-aware domain-specific content filtering system that uses a classifier to determine prompt relevance, extracts structured information using a knowledge domain ontology, and applies rules to evaluate and filter prompts before submission to the LLM, ensuring only relevant and safe queries are processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional pre-prompt filtering using keyword matching and safety classifiers is applied, then harmful content can be detected, but legitimate inquiries into topics related to harmful content categories cannot be discriminated from harmful content per se
Solution Approach 1:
The filtering system is segmented into multiple specialized components: a domain classifier to identify the knowledge domain, a safety classifier to detect harmful content, and a decision logic module to integrate their outputs. This segmentation allows each component to specialize in its function, improving both detection accuracy and domain-specific adaptability.
Solution Approach 2:
The decision logic module acts as an intermediary that receives inputs from both the domain classifier and safety classifier, evaluates their confidence scores against thresholds, and makes the final determination. This intermediary reconciles the potentially conflicting outputs of the specialized classifiers, enabling legitimate domain-specific queries to pass through while blocking actual harmful content.
2Productivity
If pre-prompt filtering is applied to all prompts, then processing load on the GAI model is reduced, but legitimate queries may be incorrectly rejected
Solution Approach 1:
The system performs preliminary classification actions before GAI model processing by using the domain classifier and safety classifier to pre-evaluate prompts. This preliminary action filters out clearly harmful content and clearly legitimate domain-specific queries, reserving GAI model resources for prompts that require its sophisticated reasoning capabilities.
Solution Approach 2:
The system dynamically adjusts the strictness of filtering based on domain classification. When a prompt is classified as belonging to a legitimate knowledge domain with high confidence, the rejection threshold is raised, making the filter more permissive. This parameter change allows the system to maintain high productivity by filtering obvious cases while preserving reliability for domain-specific legitimate queries.
3Object-affected harmful factors
If safety classifiers reject prompts with potentially unsafe content, then harmful content generation is prevented, but useful information in sensitive areas such as medicine, law, and education may be limited
Solution Approach 1:
The system applies different filtering strictness to different knowledge domains. By classifying the domain first, the system can apply more permissive safety thresholds to legitimate domains like medicine and education while maintaining strict filtering for potentially harmful domains. This local quality adjustment preserves useful information in sensitive domains while still preventing harmful content generation.
Solution Approach 2:
The system uses feedback from the domain classifier to adjust the behavior of the safety classifier. When the domain classifier identifies a legitimate knowledge domain, this feedback signals the decision logic to raise the rejection threshold, allowing more prompts to pass through to the GAI model for useful information generation while maintaining safety through the multi-stage filtering process.
Data Source
AI summary
Context-aware content filtering adapted for a knowledge domain is provided. In certain examples, a classification confidence score by a classifier indicates a level of confidence that a prompt from a user is associated with the knowledge domain. The classification confidence score is compared with a threshold. When the score is below a threshold, a violation notice is provided to the user without submitting the prompt to a generative artificial intelligence (GAI) model. When the classification confidence score is above the threshold, the prompt is further processed to determine, according to rules, whether the prompt should be submitted to the GAI model. In various examples, the rules are applied to contextual information, safety score information, and intent information derived from the prompt.