9 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includes: obtaining a historical complex question, an answer corresponding to the historical complex question, and a historical document-based knowledge base corresponding to the historical complex question, wherein the historical document-based knowledge base includes at least one historical document related to the historical complex question; extracting an entity term related to the historical complex question from the historical document, constructing a simple question based on the entity term, and determining a plurality of historical knowledge points based on the simple question and the historical complex question; and training a question generation model based on the historical document and the plurality of historical knowledge points by using a preset loss function, to obtain a trained question generation model, wherein the question generation model is used to generate a historical complex question corresponding to the historical document.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 discloses a question and answer method, a question and answer big model training method, related equipment and a program product, a configured question and answer big model can extract middle hidden layer state features of question data, a mode signal representing a reasoning mode adapted to the question data can be generated based on the middle hidden layer state features, and exemplarily, the method is simple and convenient to implement, and the efficiency is high. A short CoT reasoning mode can be generated for a simple problem, a long CoT reasoning mode can be generated for a complex problem, and then the middle hidden layer state features and the generated mode signals can be transmitted backwards for subsequent hidden layer reasoning to generate response information of problem data. Compared with a strategy of a fixed reasoning mode, the method has the advantages that reasoning accuracy can be guaranteed, reasoning efficiency can be improved, and resource utilization can be optimized.

The invention provides a data asset management method and system and related equipment, and the method comprises the following steps: a data asset management system receives a question sentence which is input by a user and is expressed in a natural language, determines a question sentence entity contained in the question sentence, carries out the retrieval in a knowledge graph based on the question sentence entity, obtains a query result, and stores the query result in a database; the knowledge graph in the data asset management system is established based on the business data in the plurality of business systems, and the business data comprises structured data, semi-structured data and unstructured data, so that the knowledge graph is established based on the business data in the plurality of business systems; according to the method, information of different departments and different data sources can be correlated and inquired, a user does not need to have chained access to multiple service systems to obtain answers, the answers can be obtained only by inputting simple questions, and the use experience of the user is improved.

The present invention relates to the technical field of artificial intelligence, and in particular to a large model-based intelligent question answering method and system and a storage medium. In the present invention, a user question is subjected to complexity evaluation by using a complexity evaluation function, and on the basis of a complexity evaluation result, different processing policies are used by means of dynamic planning and calling of an external tool to process the user question, thereby avoiding the phenomena that processing of simple questions is excessively complex and processing of complex questions is insufficient. By means of the mechanism of evaluating and iteratively optimizing the quality of a final answer, the phenomenon that the output final answer is unstable or has poor quality is avoided, and during knowledge extraction, the actual satisfaction of a user is fully considered, thereby improving the user experience.

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.