Domain-Routed Dialogue Models for Accurate Reference-Grounded Answers
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Solution Overview
Problem
Existing language models provide inaccurate or irrelevant information due to lack of specific knowledge in certain domains, leading to inefficiencies and unreliability when used in applications requiring factual correctness, such as medical or mechanical diagnostics.
Innovation Solution
A computer-implemented method using a neural network that processes input data and domain snippets to generate accurate dialogue system outputs by selecting relevant reference data based on similarity indicators, allowing for efficient generation of factual responses across multiple domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic language model is used to provide answers across multiple domains, then the model can handle diverse queries, but the accuracy and reliability of information in specific domains deteriorates
Solution Approach 1:
The patent segments the knowledge base into multiple domain-specific knowledge bases (e.g., medical, legal, technical) and uses domain identification to route queries to the appropriate knowledge base. This segmentation allows the system to maintain high reliability in specific domains while preserving versatility across multiple domains through the unified query routing mechanism.
Solution Approach 2:
The system dynamically changes the parameter of knowledge base selection based on the domain identified in the query. By analyzing query characteristics and matching them to domain profiles, the system adjusts which knowledge base is activated, thereby optimizing both the versatility to handle different query types and the reliability by using domain-specific knowledge sources.
2Reliability
If multiple domain-specific language models are trained to improve accuracy in specific areas, then the reliability of information in each domain improves, but the complexity of the system and data transmission requirements worsen
Solution Approach 1:
The patent implements a universal system architecture that can handle multiple domains through a single unified model combined with domain-specific knowledge bases. The system uses domain identification and knowledge base routing to achieve multi-functionality, eliminating the need to train and deploy separate models for each domain while maintaining high reliability through domain-specific knowledge sources.
Solution Approach 2:
The system introduces an intermediary component (domain identification module and knowledge base router) that mediates between the unified language model and multiple domain-specific knowledge bases. This intermediary directs queries to the appropriate knowledge base without requiring separate models for each domain, thereby reducing system complexity while preserving domain-specific accuracy.
3Reliability
If the corpus of training data is limited to a specific area to improve accuracy, then the reliability in that domain improves, but the amount of available training data decreases and the model becomes limited to that specific area
Solution Approach 1:
The knowledge base is segmented into multiple domain-specific sections (e.g., medical, legal, technical domains), each containing specialized training data. The system segments queries by domain and routes them to the corresponding knowledge base section, thereby achieving high reliability in specific domains while maintaining versatility across multiple domains through the segmented organization.
Solution Approach 2:
The system dynamically selects which knowledge base section to use based on the domain identified in each query. This dynamic adaptation allows the model to leverage domain-specific training data when needed while maintaining the ability to handle diverse queries across different areas, thus achieving both reliability and versatility.
Data Source
AI summary
Described herein there is a computer-implemented method for generating a dialogue system output, comprising: receiving, at an input, input data relating to a speech or text input signal originating from a user device; receiving, at one or more processors, a plurality of domain snippets, wherein each domain snippet comprises data representing reference data and data representing an associated prompt; processing, by the one or more processors, the plurality of domain snippets to determine a plurality of similarity indicators, each similarity indicator indicating a similarity between the input data and a respective domain snippet; processing, by the one or more processors, the plurality of similarity indicators to select one or more of the data representing reference data; providing, by the one or more processors, data representing the input data and the one or more data representing reference data as an input to an input layer of a neural network configured to generate a dialogue system output by: identifying at least a portion of reference data of the one or more data representing reference data for use by at least one layer of the neural network to generate a response to the input data; processing the portion of reference data to generate a dialogue system output; and outputting, by an output layer of the neural network, the dialogue system output; and outputting, by an output of a dialogue system, the dialogue system output.