Knowledge base question and answer method and system under full-process large language model small sample learning

By employing a full-process large language model few-shot learning method, and leveraging the powerful natural language capabilities of the large language model, the knowledge base question-answering process is simplified and its accuracy is improved. This addresses the problem of insufficient model robustness in existing technologies and enhances the generalization ability of knowledge base question answering.

CN118520086BActive Publication Date: 2026-06-02ZHEJIANG UNIV
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Patent Information

Application Number
CN202410662569.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-05-27
Publication Date
2026-06-02
Estimated Expiration
2044-05-27

AI Technical Summary

Technical Problem

In existing technologies, the process of knowledge base question answering is complex and the models are not robust enough, making it difficult to efficiently complete each sub-task using large language models.

Method used

The method employs a full-process large language model few-shot learning approach, leveraging the few-shot learning capability of the large language model to achieve question parsing, query template generation, knowledge base linking, and answer generation. It utilizes pre-built knowledge base information indexes and prompt words to complete various sub-tasks of knowledge base question answering.

Benefits of technology

No additional model training is required for knowledge base question answering tasks, which improves the robustness and accuracy of knowledge base question answering and enhances the model's generalization performance across different knowledge bases.

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Abstract

The application discloses a knowledge base question answering method and system under full-process large language model small sample learning, the method comprises the following steps: question analysis, identifying candidate triples and question types of original user questions through a large language model; query template generation, generating a knowledge base query statement template with placeholders for user questions; knowledge base linking, obtaining matched expressions for the placeholders in the query statement template in combination with a large language model and an entity information index that has been constructed; answer generation, generating a knowledge base query language and returning a final result. Through the small sample learning capability of the large model, the retrieval process is redesigned for the knowledge base question answering task, a model does not need to be trained separately, and the question answering process is simplified; through the powerful knowledge reserve of the large model, the question answering effect and generalization capability are improved.
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