Automated Assistant Domain Adaptation via Language Model Segmentation
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Solution Overview
Problem
Automated assistants often struggle with domain-specific vocabulary recognition and response accuracy, leading to misunderstandings and repeated interactions, as they are not tailored to specific domains, resulting in inefficient use of computing resources.
Innovation Solution
Customizing automated assistants by processing domain-specific resources to identify and aggregate pertinent information, which is then used to update their language models and speech recognition engines, allowing for improved recognition of domain-specific terms and generation of accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated assistants use general-purpose language models to handle all queries, then they can respond to a wide variety of topics, but they struggle with domain-specific vocabulary recognition and response accuracy
Solution Approach 1:
The patent segments the automated assistant system into multiple specialized language models, each trained for specific domains (e.g., medical, legal, technical). Instead of using a single general-purpose model, the system divides the assistant functionality into domain-specific components that can be selectively activated based on the query type, thereby improving both versatility and reliability in domain-specific contexts.
Solution Approach 2:
The patent creates a universal automated assistant framework that can handle multiple domains by integrating several specialized language models. The system maintains a registry of domain-specific models and dynamically selects the appropriate model based on the query, enabling the assistant to perform multiple functions across different domains while maintaining high accuracy in each specific domain.
2Reliability
If automated assistants are customized for specific domains, then they improve recognition of domain-specific terms, but they require more computing resources for processing and updates
Solution Approach 1:
The patent applies preliminary action by pre-training and caching domain-specific language models during off-peak hours or in advance. The system prepares specialized models for anticipated domains (medical, legal, technical) beforehand, so they are ready for immediate use during peak operation. This reduces the computational burden during active assistant operations while maintaining high domain-specific recognition accuracy.
Solution Approach 2:
The patent implements local quality by deploying domain-specific language models only where needed rather than using a single large model for all purposes. Each domain (medical, legal, technical) has its own specialized model with optimized parameters and vocabulary, allowing the system to use smaller, more efficient models for specific tasks instead of allocating resources for a comprehensive general-purpose model.
3Reliability
If automated assistants process all types of queries with the same level of detail, then they maintain consistent performance, but they inefficiently use computing resources on irrelevant queries
Solution Approach 1:
The patent implements dynamics by making the automated assistant system adaptive and flexible in its resource allocation. The system dynamically selects which domain-specific language model to use based on the incoming query's characteristics, and can adjust the level of processing detail based on query complexity and domain relevance. This allows consistent performance across different query types while optimizing resource usage by applying appropriate processing levels.
Solution Approach 2:
The patent applies parameter changes by adjusting the operational parameters of the language model based on the query type and domain. The system can change parameters such as model selection, processing depth, and response detail level to match the specific requirements of each query. This maintains consistent performance quality while improving productivity by avoiding unnecessary processing for simple or irrelevant queries.
Data Source
AI summary
Implementations herein related to customizing an automated assistant using domain-specific resources. One or more resources are processed to generate a natural language representation of the contents of the resources. The natural language representation is utilized to customize an automated assistant for interactions with a user. Various implementations include priming and fine-tuning large language models that are utilized to implement the automated assistant. Various implementations are directed to biasing speech recognition based on terms identified in the resources. Various implementations are directed to customizing the tone of the automated assistant based on information included in the resources.