21 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 system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and-Answer system with the possibility of extending to more complex generative AI interactions.

The invention discloses a collaborative response method and device for large and small models, equipment, a medium and a program. The method comprises the steps of calling a low-rank adaptation small model to generate an initial response through an edge node when an input instruction of a user is received; calculating the whole sentence confidence coefficient in the initial response; if the whole sentence confidence coefficient is smaller than a sentence-level confidence coefficient threshold value, a collaborative response instruction is sent to the cloud server cluster; selecting a specified number of target large models from the plurality of large models, and correcting at least one lexical element based on a lexical element level confidence coefficient threshold to obtain a plurality of collaborative responses; and selecting the target collaborative response with the highest confidence as a real response, and returning the real response to the user. According to the embodiment of the invention, the large model and the small model are utilized to cooperatively work to quickly give a preliminary reply to a simple question, the large model intervenes in optimization when a complex question is encountered, a satisfactory answer is provided for a user, the user experience is improved, and the instruction processing capability is enhanced by performing fine tuning optimization on the low-rank adaptive small model.

Systems and methods relate to executing a task using a machine learning model based on prompt generation and collaborative interactions with a user. The machine language model generating a set of questions based on a task request. The user interactively answers the questions. A task processor generates a set of question-answer pairs based on the questions generated by the machine learning model and the answers given by the user. The machine learning model generates a task specific output based on the set of question-answer pairs. The machine learning model represents a large language model with deep learning. The simple question-and-answer prompts enable non-expert users to instruct the machine learning model with information that is sufficient to execute the task without overwhelming the users with the operations. The machine learning model leverages the answers to execute the task with accuracy, thereby providing efficacy of the prompting technique.

The invention relates to a knowledge question-answering method and device, electronic equipment and a storage medium, and is applied to the technical field of computers, and the method comprises the following steps: obtaining a to-be-answered target question; determining a difficulty type of the target question, wherein the difficulty type comprises a simple question and a complex question; under the condition that the target problem is a complex problem, determining a target solving mode of the target problem from a plurality of preset solving modes; and answering the target question according to the target solving mode to obtain answer information of the target question.

The embodiment of the invention provides a medical information processing method, an information processing method, equipment, a storage medium and a program product. In the embodiment of the specification, an automatic model selection mechanism is provided, and cooperative work of an inference model and a non-inference model is realized through a problem classification model. The problem classification model is used as a'routing center ', and the user requests are intelligently distributed to different medical question and answer models for processing according to the types of the medical problems, so that the advantages of the different medical question and answer models are fully played, and optimal configuration of system resources and improvement of overall performance are realized. Besides, intelligent routing is carried out to the corresponding medical question and answer model for question answering, the answering quality is enhanced, quick response of simple questions and deep analysis of complex questions are ensured, and optimal configuration of system resources is realized. In addition, the user can obtain the medical answer information without selecting knowledge by a professional model, so that the answering efficiency and the user satisfaction of the medical question-answering system are improved.

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.

Systems and methods relate to executing a task using a machine learning model based on prompt generation and collaborative interactions with a user. The machine language model generating a set of questions based on a task request. The user interactively answers the questions. A task processor generates a set of question-answer pairs based on the questions generated by the machine learning model and the answers given by the user. The machine learning model generates a task specific output based on the set of question-answer pairs. The machine learning model represents a large language model with deep learning. The simple question-and-answer prompts enable non-expert users to instruct the machine learning model with information that is sufficient to execute the task without overwhelming the users with the operations. The machine learning model leverages the answers to execute the task with accuracy, thereby providing efficacy of the prompting technique.

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 discloses a complex question-answering method and system for mineral knowledge graphs, comprising: constructing a complex mineral question-answering dataset based on an existing simple mineral question-answering dataset; constructing a text representation model based on the existing mineral knowledge graph and dataset; utilizing the text representation model and dataset to construct a mineral question center word recognition, entity disambiguation, and reasoning model; using the trained mineral question center word recognition and entity disambiguation model to perform center word recognition and entity disambiguation on user-input questions, locate the reasoning starting entity, and use the reasoning model to infer the answer from the mineral knowledge graph using the entity as the starting point and return it to the user. The method and system provided by the present invention solve the problem that the current mineral knowledge graph can only perform simple question-answering with single-hop reasoning and cannot answer complex questions involving multi-hop relationships. It achieves accurate answers to mineral questions input in natural language and can be used for intelligent query of mineral knowledge graphs.

This specification provides a question-answering method, apparatus, electronic device, and medium based on a large language model. The method includes: performing a knowledge base retrieval on the original question to obtain multiple first retrieval results and the retrieval scores of the multiple first retrieval results; in the case where there are first retrieval results with retrieval scores less than the score threshold among the multiple first retrieval results, using the large language model to determine whether the original question is a simple question; if the original question is a simple question, performing query expansion processing on the original question to obtain a new question; performing a knowledge base retrieval on the new question to obtain second retrieval results; and determining an answer message based on the second retrieval results and the first retrieval results among the multiple first retrieval results with retrieval scores greater than or equal to the score threshold.

A programming language designed for building rich form applications, including creating various forms. The language comprises modular components that streamline the definition of questions, form structures, data flow logic, and functions for forms. The language supports simple questions and compound questions, which can be object-like or array-like. Forms defined in this language are flexible, with customizable styles and layouts adaptable to various user interfaces. Data flow rules automate runtime form state changes, while form functions enable powerful runtime computation on form state and responses, thereby allowing automatic response analysis and multi-form workflow. The language provides common, modular, reusable, reliable function units for the developers to build form applications. The language promotes form portability across vendors and applications, enabling a growing set of form applications that users can freely switch between without losing their form data and fostering a community for form sharing and form advancement by all form stakeholders.

A system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative Al systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and- Answer system with the possibility of extending to more complex generative Al interactions.

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 application discloses a zero sample visual question answering method and system based on Boolean prompt enhancement, relates to the technical field of zero sample visual question answering, and comprises the following steps: judging the complexity of an input question based on a pre-trained visual language model, outputting the answer to a simple question, and selecting a question with high complexity for the model; extracting keywords based on the selected complex question, and generating a description of an image corresponding to the question; generating a sub-question capturing global information based on the original question, generating a sub-question containing local information from the keywords of the question and the image description; constructing a redundancy value and a richness value of the sub-question based on the cosine similarity between the generated sub-question and the original question; deleting the sub-question with high redundancy by using the redundancy value, ensuring the richness of the content of the sub-question by using the richness value, using the obtained sub-question as a prompt to enhance the understanding of the complex question and the attention to local visual information of the model, and improving the accuracy of zero sample visual question answering.

The application relates to the technical field of data processing, and discloses a self-adaptive question recognition method and system, which comprises the following steps: carrying out question segmentation on a document to obtain question data corresponding to the document; using a large language model to analyze the obtained question data to obtain question data of different question types and question parameters corresponding to each question data; and according to a predetermined data structure, carrying out data structural processing on the obtained question data of different question types and the corresponding question parameters to obtain structured and encapsulated question data. The application can adjust the complex rules in the data preprocessing stage into simple question segmentation identification, and uses a large language model to analyze and process the questions, so that the time input of business in the data processing stage can be greatly reduced.

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 embodiments of this specification provide a data processing method and apparatus. The method includes: determining at least one problem reasoning step corresponding to an initial problem, and a reasoning answer corresponding to the initial problem obtained based on the at least one problem reasoning step; performing a correctness verification on the at least one problem reasoning step and the reasoning answer according to the initial answer corresponding to the initial problem to obtain a verification result; obtaining target data according to the verification result, the initial problem, and the at least one problem reasoning step, where the target data includes the initial problem, target reasoning steps, and a target answer corresponding to the initial problem; compared with a simple question-and-answer pair including the initial problem and the initial answer, the target data adds target reasoning steps, thereby realizing the extension of the long reasoning link for the simple question-and-answer pair, and providing effective training data for improving the model reasoning ability subsequently.

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.