Question Answering with Vector Matching and Semantic Verification
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Solution Overview
Problem
Intelligent question-answering platforms struggle to accurately understand user questions, leading to low accuracy in provided answers due to insufficient semantic matching between question sentences.
Innovation Solution
A question-answering method that utilizes a vector database to identify candidate question sentences with high vector matching degrees and employs a large language model to confirm semantic similarity, ensuring accurate answers are retrieved from the database or a knowledge base when semantic matches are found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional question-answering methods are used, then the system is simple to operate, but the accuracy of understanding user questions and providing answers is low
Solution Approach 1:
The question-answering process is divided into multiple stages: vector representation of questions, vector database matching to find candidate questions, semantic similarity verification using large language models, and answer retrieval. This segmentation allows each component to specialize in one aspect, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
Vector representations serve as intermediaries between user questions and the question-answering database. The vector database acts as an intermediary layer that transforms semantic matching into mathematical operations, enabling more accurate and efficient question matching while simplifying the overall process.
2Speed
If vector database matching is used to find candidate questions, then the speed of question matching is improved, but the precision of semantic understanding may be compromised
Solution Approach 1:
The vector database performs preliminary matching to quickly identify candidate questions that are potentially relevant to the user's query. This preliminary action filters out obviously unrelated questions, narrowing down the search space before applying more computationally intensive semantic analysis, thus achieving both speed and precision.
Solution Approach 2:
The system uses a two-stage feedback mechanism: first, vector similarity scores provide initial feedback on candidate relevance; second, large language models provide refined semantic feedback to confirm or reject candidates. This multi-layered feedback ensures both rapid filtering and accurate final selection.
Data Source
AI summary
The present application discloses a question-answering method. The method includes: acquiring a to-be-processed question sentence; determining, from a question-answering database, at least one candidate question sentence matching the to-be-processed question sentence, wherein the question-answering database includes the at least one candidate question sentence and an answer corresponding to each of the at least one candidate question sentence; determining whether semantics of the to-be-processed question sentence and semantics of the at least one candidate question sentence are same; and in response to a candidate question sentence with same semantics as the to-be-processed question sentence exists in the at least one candidate question sentence, acquiring, from the question-answering database, an answer corresponding to the candidate question sentence with the same semantics as the to-be-processed question sentence, and taking the answer as an answer corresponding to the to-be-processed question sentence.