Dialogue Generation System with Knowledge Retrieval and Evaluation
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Solution Overview
Problem
Current dialogue generation solutions perform poorly in complex and changing language environments and knowledge requirements, leading to low performance in various AI applications such as intelligent customer service and online education.
Innovation Solution
A method that acquires a current question statement and historical dialogue information, retrieves relevant knowledge items from a knowledge base, and uses a generative model to produce a reply statement, which is then evaluated for accuracy and coherence before output, ensuring the response aligns with user intent and professional knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a generative model is used to generate dialogue responses, then the fluency and naturalness of responses are improved, but the accuracy and reliability of responses deteriorate
Solution Approach 1:
The patent introduces a knowledge base as an intermediary component between the user input and the generative model. The system retrieves relevant knowledge items from the knowledge base and uses them to guide and constrain the generative model's output, ensuring that responses are both fluent and factually accurate. This mediator approach allows the system to maintain natural language generation while grounding responses in verified knowledge.
Solution Approach 2:
The patent merges multiple components into an integrated dialogue generation system: a knowledge base for factual information, a retrieval module for selecting relevant knowledge, a generative model for natural language production, and an evaluation module for quality assessment. This combination allows the system to leverage the strengths of each component while mitigating their individual weaknesses.
2Reliability
If knowledge base retrieval is performed to improve response accuracy, then the reliability of responses is improved, but the response time increases
Solution Approach 1:
The patent performs preliminary actions by pre-organizing knowledge into a structured knowledge base with defined schemas and relationships before dialogue generation occurs. During actual dialogue, the system quickly retrieves pre-processed knowledge items rather than processing raw information in real-time, significantly reducing response time while maintaining accuracy.
Solution Approach 2:
The patent extracts only the most relevant knowledge items from the knowledge base based on the user's question and dialogue context, rather than retrieving or processing all available knowledge. This selective extraction approach minimizes the time required for knowledge retrieval while ensuring that the most pertinent information is used for generating accurate responses.
3Reliability
If evaluation and verification steps are added to ensure response quality, then the reliability of responses is improved, but the system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the evaluation module assesses generated responses against retrieved knowledge items and dialogue context. The evaluation results feed back into the generation process, allowing the system to refine and improve responses. This closed-loop approach ensures high response quality while using iterative refinement rather than complex multi-layer verification systems.
Data Source
AI summary
A method for generating a dialogue includes acquiring a current first question statement and historical dialogue information associated with the first question statement; acquiring, from a knowledge base, a first knowledge item associated with the first question statement and a second knowledge item having a question-answer relationship with the first knowledge item; obtaining a first reply statement output by a generative model by inputting the first question statement, the first knowledge item, and the historical dialogue information into the generative model; evaluating the first reply statement based on the first question statement, the first knowledge item, and the second knowledge item; and outputting the first reply statement in response to the first reply statement passing evaluation.