Multi-Domain Language Model Adapters for Content Moderation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems require multiple content moderation models for each domain and chatbot, leading to high costs and inefficiencies in maintaining and developing these models, especially when new domains or chatbots are added.
Innovation Solution
A multi-domain language model is trained with base layers, domain general adapter layers, and domain specific adapter layers, using unlabeled and labeled datasets to generate a single model capable of handling various domains without sacrificing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple content moderation models are developed for each domain and chatbot, then content moderation accuracy for specific domains is improved, but the cost and complexity of maintaining and developing models increases
Solution Approach 1:
The patent applies universality by creating a single content moderation model that can handle multiple domains and chatbots through domain-specific adapter layers. The base model serves multiple functions across different domains (e.g., healthcare, finance, education) without requiring separate dedicated models for each, thereby reducing overall system complexity while maintaining domain-specific accuracy through specialized adapters.
Solution Approach 2:
The patent segments the content moderation system into a shared base model and domain-specific adapter layers. The base model handles general content moderation tasks, while adapter layers are appended to handle domain-specific nuances. This segmentation allows the system to maintain accuracy for specific domains while avoiding the need to develop and maintain entirely separate models for each domain.
2Reliability
If multiple content moderation models are maintained for different domains, then domain-specific abuse detection is improved, but the time and resources required for model development and maintenance increase
Solution Approach 1:
The patent applies preliminary action by pre-training a robust base model on diverse datasets that cover multiple domains and abuse types. This pre-trained base model serves as a foundation that can be quickly adapted to new domains through adapter layers, eliminating the need to start from scratch when a new domain is added. The base model's knowledge is prepared in advance, allowing rapid deployment of domain-specific variants.
Solution Approach 2:
The patent merges the strengths of domain-specific models with a shared base model. Instead of maintaining separate complete models for each domain, the system combines a universal base model with domain-specific adapter layers. This merging approach preserves domain-specific abuse detection capabilities while sharing computational resources and training infrastructure across all domains, thereby reducing overall development and maintenance time.
3Device complexity
If a single multi-domain content moderation model is trained, then the number of models to maintain is reduced, but the challenge of training on diverse domains increases
Solution Approach 1:
The patent segments the training process into distinct stages: pre-training the base model on diverse unlabeled data from multiple domains, then fine-tuning domain-specific adapter layers on labeled domain-specific data. This segmentation makes the training process more manageable by dividing it into independent phases, where the base model is trained once on comprehensive data, and subsequent domain-specific training only requires training the lightweight adapter layers.
Solution Approach 2:
The patent uses domain-specific adapter layers as intermediaries between the base model and domain-specific tasks. These adapters act as mediators that translate general base model capabilities into domain-specific applications without requiring the entire base model to be retrained for each domain. The adapters serve as an intermediary layer that simplifies the training process by isolating domain-specific learning to small, manageable components.
Data Source
AI summary
A method including receiving a multi-domain language model having a number of base layers, a number of domain general adapter layers, and a set of domain specific adapter layers. The method also includes training the base layers on an unlabeled training dataset. The method also includes training the domain general adapter layers on a domain general labeled dataset generated from the unlabeled training dataset. Training the domain general adapter layers excludes updating the set of domain specific adapter layers. The method also includes training the set of domain specific adapter layers on a domain specific labeled dataset generated from the unlabeled training dataset. Training the set of domain specific adapter layers excludes updating the domain general adapter layers. The method also includes returning, as a trained multi-domain language model, the updated base layers, the updated domain general adapter layers, and the updated set of domain specific adapter layers.