5 results about "Simple question" patented technology

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

A question generation model training method, includes: obtaining a historical complex question, an answer corresponding to the historical complex question, and a historical document-based knowledge base corresponding to the historical complex question, wherein the historical document-based knowledge base includes at least one historical document related to the historical complex question; extracting an entity term related to the historical complex question from the historical document, constructing a simple question based on the entity term, and determining a plurality of historical knowledge points based on the simple question and the historical complex question; and training a question generation model based on the historical document and the plurality of historical knowledge points by using a preset loss function, to obtain a trained question generation model, wherein the question generation model is used to generate a historical complex question corresponding to the historical document.

The invention provides an intelligent question answering method and electronic equipment, and the method comprises the steps: determining whether an input question relates to mathematical operation or not as a simple question or a complex question, and directly generating an answer for the simple question through a corresponding agent, for complex questions, corresponding agents and external calculation tools are used together to generate answers. According to the scheme, the defect that mathematical questions cannot be accurately answered by traditional intelligent questions and answers is overcome, so that complex questions covering the mathematical questions can be accurately answered; according to the scheme, whether the question is simple or complex is firstly distinguished, and different processing strategies are adopted for different types of questions, so that subsequent reasoning is more suitable for the different types of questions, and the question and answer requirements of diversified questions are met.

The invention discloses a knowledge graph question and answer reasoning method and system based on self-adaptive double-feature fusion. The method comprises the steps that natural language questions of a user are collected; a self-adaptive dual-mode feature fusion module is introduced, and a knowledge graph reasoning question and answer model is constructed; reasoning the natural language question of the user based on the knowledge graph reasoning question and answer model, and generating a question answer and a reasoning path of the user. According to the method, the accuracy of the knowledge graph question-answering system in a complex reasoning scene and the response speed of simple questions can be improved. The knowledge graph question and answer reasoning method and system based on self-adaptive double-feature fusion can be widely applied to the technical field of knowledge graph reasoning.

The one or more embodiments of the specification disclose a training method of a question generation model. The method first acquires a historical complex question and a corresponding answer, and a historical document type knowledge base corresponding to the historical complex question, and the historical document type knowledge base at least includes a historical document related to the historical complex question. Then, an entity word related to the historical complex question is extracted from the historical document, a simple question is constructed based on the entity word, and a plurality of historical knowledge points are determined based on the simple question and the historical complex question. Finally, the question generation model is trained based on the historical document and the plurality of historical knowledge points through a preset loss function to obtain a trained question generation model, wherein the question generation model is used to generate a historical complex question corresponding to the historical document.