4results about How to "Accurate answer" patented technology

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.