8results about How to "Accurate answer" patented technology

The invention discloses an intelligent sales method, device and equipment based on a large model Agent and a medium, and relates to the field of intelligent sales, the method comprises the following steps: using a large language model to carry out intention analysis on a user problem, determining whether a tool matched with the user problem exists, and obtaining a judgment result; if the judgment result is no, generating an answer to the user question by using a large language model and a system instruction, and feeding back the answer to the user; and if the judgment result is yes, determining a tool matched with the user question as a target tool, calling an external resource corresponding to the target tool by utilizing the target tool, generating an answer to the user question, and feeding back the answer to the user. According to the method, the practicability, the efficiency and the real-time performance of the large language model in a sales environment are improved, and the difficulty of intention recognition is reduced.

The invention relates to an AI online education intelligent question and answer information processing method, which comprises the following steps of S1, collecting teaching materials, and performing preprocessing and labeling to form a text data set with labels; s2, constructing a question and answer model based on XLNet; s3, introducing a specific context optimization question and answer model for a specific subject or topic to obtain an optimized question and answer model; s4, constructing an auxiliary understanding information knowledge graph by considering hierarchical design of age groups based on the labeled text data set; s5, inputting a query by the user, and obtaining an optimally matched answer based on the optimized question and answer model; according to the age group of the user and the best matching answer, auxiliary understanding information is obtained from the knowledge graph, and a complete answer is obtained; and S6, adjusting provided content and difficulty through user interaction data, and recommending related domain knowledge based on user behaviors and preferences. According to the method, the direct answers required by the user are provided, the supplementary background information is provided to enhance the understanding, and the requirements of the user are presented in a personalized manner according to the characteristics of the user.

The invention discloses a construction method, system and equipment of a power dispatching knowledge question-answering system and a storage medium. The construction method comprises the steps of obtaining a power dispatching semantic document needing to be processed by the power dispatching knowledge question-answering system; sequentially carrying out character-based segmentation, proposition conversion and proposition combination on the power dispatching semantic document on the basis of the large language model and the related cue words to obtain a plurality of text blocks; based on the large language model and the related prompt words, identifying and extracting entities related to power dispatching and relationships among the entities from each text block; constructing a local graph for each text block on the basis of the large language model and the related prompt words, the entities extracted from each text block and the relationship among the entities; traversing each text block, and combining all the local graphs into a global graph; and on the basis of the large language model and the related prompt words, according to the question type queried by the user, a reply text is correspondingly generated by matching a minimum local graph which is most related to the global graph in the global graph.

The present invention discloses a method and device for realizing intelligent customer service, and specifically relates to the technical field of intelligent customer service, including an intelligent customer service realization data acquisition module, an intelligent customer service realization data analysis module, an intelligent customer service realization intelligent evaluation module, an intelligent customer service realization intelligent comprehensive analysis module and an intelligent customer service realization intelligent human-computer interaction module; the intelligent customer service realization data acquisition module is used to collect intelligent performance data of the intelligent customer service, obtain various monitoring parameters for evaluating the intelligent performance, and transmit the various monitoring parameters to the intelligent customer service realization data analysis module; the present invention analyzes the intelligent parsing parameters to obtain an indicator to measure the ability of the intelligent customer service to understand and handle user problems in the realization of intelligence, thereby helping to improve the accuracy of the intelligent customer service system's understanding of user input, processing efficiency and overall service quality.

The application discloses a kind of causal common sense knowledge base construction methods based on graph attention mechanism, comprising the following steps: 1) obtain causal common sense knowledge triples from open knowledge base as training resources;2) using knowledge graph embedding technology, obtain the vector representation of causal common sense knowledge triples;3) combined with background knowledge graph, construct causal common sense knowledge base construction model based on graph attention mechanism, select appropriate loss function to optimize model parameters;4) using causal common sense knowledge base construction model to score the confidence of missing triples, the highest score triples are added to the knowledge base as new learning causal common sense knowledge.The application is based on background knowledge graph, and constructs causal common sense knowledge base construction model using graph attention mechanism, intends to learn new causal common sense knowledge through a small amount of training resources, expands causal common sense knowledge base, so that the knowledge base can better support intelligent question and answer system.

The application provides a large language model-based question and answer method, device, equipment and medium. The application introduces Chosen reinforcement constraints and Rejected weakening constraints to optimize the large language model, uses Chosen reinforcement constraint parameters to maintain the stable generation ability of the large language model for Chosen answers, effectively maintains or even improves the generation quality of the large language model. The use of Rejected weakening constraint parameters effectively suppresses the generation of incorrect or unreliable low-quality answers, avoiding the reduction of the generation quality of the large language model. The two work together to effectively improve the tendency of the large language model to generate high-quality answers and maintain the stability of the output answer quality, so that the large language model can stably output accurate and reliable answers in mathematical reasoning, code generation and other question and answer scenarios, effectively solving the problem of output quality fluctuation and insufficient accuracy in existing question and answer scenarios.

This application provides a data processing method, apparatus, and electronic device based on a natural language model, belonging to the field of financial technology. The method includes: acquiring financial data information and information about questions to be answered; representing the financial data information using knowledge representation to create a first data structure and a second data structure; creating an insurance dataset based on the first and second data structures; training a preset natural language model using the insurance dataset to obtain an automatic question-answering language model; responding to a user's search instruction by performing text analysis on the information about questions to be answered to obtain analysis results; retrieving answers based on the automatic question-answering language model and the insurance dataset to obtain a search set; and converting the user's search instruction into a request to transfer the user to human assistance when the search set is empty or does not contain a preset answer statement. This application can improve the efficiency and quality of problem handling and service.

The application discloses a fire-fighting business question and answer method based on a large model and related equipment, and the method comprises the following steps: constructing a professional knowledge base through a knowledge acquisition component; combining a pre-trained language model with the professional knowledge base to construct a fire-fighting industry professional large model and a retrieval-enhanced generated knowledge base; obtaining multi-modal problem information and a target access permission label list through a multi-modal information interaction module; generating a prompt word template according to the multi-modal problem information and the target access permission label list through a multi-modal information analysis module; generating an initial answer based on the fire-fighting industry professional large model in combination with the retrieval-enhanced generated knowledge base and the prompt word template through a professional knowledge answer generation module; screening the initial answer through the multi-modal information interaction module to obtain a target answer, and outputting the target answer on a visual interface. The application can efficiently and accurately obtain the required fire-fighting industry professional knowledge, and can be widely applied to the field of artificial intelligence technology.