Multi-Domain Language Model Moderation With Domain Adapter Routing
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Solution Overview
Problem
Existing content moderation models for language models are application-specific and require multiple models for different domains, leading to high maintenance and development costs, and no single model is accurate enough to moderate multiple language models effectively.
Innovation Solution
A trained multi-domain language model is used to moderate content across multiple domains by applying domain general and specific adapter layers, generating an output decision, and routing queries based on the decision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple domain-specific moderation models are developed and maintained, then content moderation accuracy for each specific domain is improved, but development and maintenance costs increase significantly
Solution Approach 1:
The patent implements a universal moderation model that can handle multiple domains through adapter layers. The base model serves multiple functions across different domains (e.g., healthcare, legal, finance) by dynamically loading domain-specific adapter layers, eliminating the need for separate moderation models for each domain while maintaining high accuracy.
Solution Approach 2:
The moderation system is segmented into a base model and separate adapter layers for different domains. Each adapter layer contains domain-specific knowledge and can be independently loaded or unloaded based on the query domain, allowing the system to maintain multiple domain specializations without requiring multiple complete models.
2Device complexity
If a single moderation model is used across multiple domains, then development and maintenance costs are reduced, but content moderation accuracy decreases
Solution Approach 1:
The patent applies local quality by making different parts of the model (adapter layers) domain-specific while keeping the base model general. When moderating queries in a specific domain, the corresponding adapter layer is activated to provide domain-specific moderation expertise, ensuring high accuracy for each domain while using a single base model structure.
Solution Approach 2:
The system dynamically selects and activates appropriate adapter layers based on the query domain. The moderation model transitions from a static single-model approach to a dynamic multi-domain approach where the effective model configuration changes based on the input domain, maintaining accuracy without requiring multiple permanent model instances.
3Measurement precision
If domain-specific training data is used for each moderation model, then domain-specific moderation performance is improved, but training data requirements and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-training the base model on general moderation tasks and then efficiently adapting it to specific domains through adapter layers. The adapter layers are trained on domain-specific data separately and can be attached to the pre-trained base model, avoiding the need to retrain the entire model for each domain and significantly reducing training time.
Data Source
AI summary
A method including receiving a query for a primary language model. An inference prompt is generated and a query domain is identified. A trained multi-domain language model is applied to the inference prompt according to the inference prompt and the query domain to generate an output decision. The query is routed to a routing process according to the output decision.