1419 results about "Question answer" 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.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

Provided in the embodiments of the present disclosure are a question answering processing method and system, a device and a storage medium. The method comprises: on the basis of a user question, a first prompt template containing first tool set information, and an LLM, a first agent acquires, successively generated by the LLM, a plurality of pieces of solving task information that are required to be executed by invoking a second agent and summary task information that is required to be executed by the first agent, and successively transmits the plurality of pieces of solving task information to the second agent; on the basis of received current solving task information, a second prompt template containing second tool set information, and the LLM, the second agent acquires task execution information that is generated by the LLM and corresponds to the current solving task information, and accordingly acquires from an application server a task execution result of the current solving task information; and the first agent summarizes the task execution results of the plurality of pieces of solving task information, so as to determine reply information corresponding to the user question. By means of the cooperation of different agents, user questions are gradually and accurately solved.

The invention discloses a double-engine government affair question and answer method based on large model fine tuning and RAG retrieval, belongs to the field of government affair digitization and natural language processing, and combines large model language understanding generation ability, retrieval enhancement generation technology and a structured reasoning mode. The defects of a traditional government affair question and answer method in the aspects of dynamic policy response, complex semantic understanding and compliance control are overcome. Government affair field knowledge is adapted through large-model fine adjustment, and high-precision and timeliness answering of government affair consultation is realized in combination with vector retrieval and a dynamic updating mechanism. The core innovation of the method lies in deep fusion of a double-engine architecture and dynamic knowledge management, the accuracy and response efficiency of government affair questions and answers are improved on the premise of ensuring policy compliance, and the method is suitable for intelligent upgrading of scenes such as government affair service halls and online consultation platforms.

Disclosed is an industrial question answering model training method based on reinforcement learning and knowledge base matching, comprising the following steps: S1, collecting professional knowledge questions and answers in an industrial field to construct an industrial knowledge base, training a reward model, carrying out, for industrial knowledge questions and answers, matching comparison on outputs of an industrial question answering model and content of the industrial knowledge base, and obtaining reward values on the basis of similarities; S2, sorting the reward values, and using a sorting loss function to train and update parameters of a reward model network; and S3, carrying out industrial question answering model training, incorporating a penalty term for the reward values, and using a reinforcement learning algorithm to train the industrial question answering model multiple times to obtain an optimal strategy. According to the industrial question answering model training method based on reinforcement learning and knowledge base matching of the present invention, the reinforcement learning algorithm is used, and iterative training is carried out multiple times, thereby helping the industrial question answering model to learn and understand industrial professional knowledge and improving the question answering accuracy of the industrial question answering model.

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention discloses a reverse question guide question answering implementation method and system, and belongs to the technical field of artificial intelligence and natural language processing. Context-aware intention dynamic correction is realized through a three-level intention classification system, and cross-modal knowledge matching is realized by adopting a distributed semantic index technology; based on the reinforcement learning strategy, optimizing a cooperative work mechanism of the dialogue strategy and the knowledge base; comprising the steps of intention recognition: analyzing a session of a user by using an intention recognition model, and constructing a three-level intention classification system based on deep semantic understanding, including main class recognition, fine-grained analysis and context perception; question rewriting: constructing a dynamic rewriting engine to rewrite the user question; recalling and cleaning multi-source item knowledge; generating a reverse question; locking items and acquiring item data; generating questions and answers. According to the method, the robustness, the real-time performance and the scene adaptation capability of a professional question answering system can be improved, and the government affair service question answering accuracy, the intention recognition precision and the cross-region recommendation adoption rate are improved.

The invention relates to a low-altitude intelligent question and answer construction method and system based on dynamic parameters. The method comprises the following steps: collecting low-altitude domain data, cleaning the low-altitude domain data, generating a semantic vector index, and constructing a low-altitude domain knowledge base based on the semantic vector index; receiving a natural language query of a user, analyzing a query intention, extracting keywords in the natural language query, and matching a corresponding candidate word quantity based on query types of the natural language query of the user, the query types at least comprising high-frequency phrase query and low-frequency long-tail query; and respectively carrying out fusion semantic retrieval and keyword retrieval, carrying out secondary sorting on the candidate results based on a preset resorter, preferentially sorting the candidate results related to the query intention, and outputting the corresponding candidate results. By adopting the method, a combined domain retrieval enhancement generation mechanism is provided, so that the professionality and accuracy of answers are improved; and the retrieved knowledge base content is re-screened to increase the hit probability of the knowledge base.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent question asking method and device, based on a large model and semantic modeling, the method comprises the following steps: S1, a user interaction module receives a natural language question input by a user; s2, the semantic modeling module collects and integrates related data sources; s3, an analysis rule is built in the rule analysis module; s4, the large model processing module understands and answers the natural language question input by the user; s5, the SQL conversion module generates a universal logic SQL for the natural language problem input by the user through rules and natural language analysis; s6, the data processing and indexing module establishes a data indexing system; s7, establishing a dynamic feedback and learning mechanism; and S8, establishing a security and privacy protection mechanism. Compared with the prior art, the accuracy and timeliness of intelligent question answering of the user questions can be improved, and accurate answering of specific big data scene tasks is achieved.

The invention relates to the technical field of intelligent retrieval, in particular to an enterprise RAG intelligent question answering system based on vector database reordering. The system specifically comprises: a document recall module, which retrieves a vector database to obtain candidate document blocks containing business metadata; the comprehensive scoring module is used for calculating a semantic correlation score by adopting a later-stage interaction architecture based on bidirectional token importance weighting, performing path semantic matching and context sensing rule evaluation according to a preset metadata ontology graph to obtain a service attribute score, and analyzing the evidence sub-graph to obtain a fact path score; fusing the semantic correlation score, the service attribute score and the fact path score to generate a comprehensive correlation score; and the sorting output module performs optimization resorting based on a preset punishment mechanism and the comprehensive correlation score to generate an optimized context set, and calls a generation model to output answers based on the optimized context set. According to the method, semantic accuracy, business compliance and fact reliability can be considered, and more trustworthy high-quality enterprise-level answers can be generated.

The invention provides an optimization method and device for a retrieval enhancement generation system, equipment and a medium. Comprising the following steps: constructing model training data according to a document extracted from a knowledge base and text fragments obtained by segmenting the document; performing model parameter optimization on the vectorization model based on the model training data; processing the user question and the text fragment based on a vectorization model to obtain a retrieval fragment; according to the retrieval fragment, the user question and the session context, configuring a cue word; calling a generation model to process the user question and the cue word, and generating a question answer text; a supervised fine tuning and reward feedback training mode is adopted, and a generation model is optimized according to a question and answer data set formed by a standard answer text, a user question and a text retrieved according to the user question; and determining a target evaluation index according to the question answer text, the manual annotation data of the user question and the question and answer evaluation index set, so as to evaluate the performance of the retrieval enhancement generation system according to the target evaluation index.

The invention provides a minority language professional question and answer method based on mixed retrieval and retrieval enhancement generation, which comprises the following steps: S1, constructing a multi-language knowledge constructing a vector index database and a term knowledge graph by using a multi-language model according to a related minority language document; s2, multi-layer mixed retrieval: cross-language document recall is realized through a multi-layer mixed retrieval module, and the multi-layer mixed retrieval module is composed of keyword retrieval, semantic retrieval and vector retrieval; s3, answer generation: performing answer generation through an adaptive multi-language model by using a retrieval enhancement generation module, and introducing rule constraint decoding and a dynamic attention mechanism in the generation stage to improve the professionality and accuracy of the answer; and S4, self-adaptive optimization and knowledge updating: through a user feedback reinforcement learning module, optimizing the model based on user error correction data and supervising updating of the knowledge base. According to the method, professional questions and answers of the minority language can be realized based on a small amount of professional data of the minority language, the recall rate of the document of the minority language is improved, and the generation quality is improved.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph retrieval reasoning method and system based on big language model enhancement, and the method comprises the steps: carrying out the entity extraction of a user question through a big language model, and generating a candidate entity list; retrieving entity nodes corresponding to the candidate entity list in a knowledge graph database through a mixed retrieval method of fuzzy matching and vector retrieval; re-screening the entity nodes by using a large language model to generate a starting node list; performing width-first traversal on each node in the starting node list in an edge-in direction and an edge-out direction, and retrieving a reasoning path in the knowledge graph; constructing the reasoning path into a prompt template, and inputting the prompt template into a large language model to generate an answer to the user question; according to the method, the big language model is utilized to analyze questions and query the knowledge graph in the knowledge graph question-answering scene, question-answering is performed based on the knowledge graph path, and the effectiveness and accuracy of user knowledge graph question-answering are improved.

The present application relates to the technical field of artificial intelligence, and in particular to a question answering method based on a large model, and an electronic device. The electronic device comprises a communication interface, a memory, and a processor, the memory is configured to store a computer instruction, and the processor is configured to execute a computer program to enable the electronic device to: determine, on the basis of each pre-stored text block, a target text block matched with a question to be answered; obtain, if non-text data including a picture and / or a table is pre-stored for the target text block in advance, stored summary text of the non-text data; and input the question to be answered, the target text block, and the summary text into a first large model on the basis of a preset format to obtain answer information. Since the summary text is text that summarizes all content in the non-text data, the first large model does not need to process the picture and / or the table and still can ensure that the content recorded in the picture and / or the table is taken into consideration during generation of the answer information, thereby improving the model-based question answering accuracy.

The invention provides a Cypher-stack type alignment generation method based on a large language model, and belongs to the field of knowledge graph questions and answers. The method comprises the steps that user query and knowledge graph mode information are input into the large language model to generate an initial Cypher query statement; based on a thinking chain prompt technology, retrieving from the knowledge graph to obtain a retrieval result related to user query, and correcting the initial Cypher query statement by the large language model based on the retrieval result to obtain a corrected Cypher query statement; and executing the corrected Cypher query statement. The invention further provides a Cypher-stack type alignment generation device based on the large language model. Through the semantic analysis capability of a large language model and a thinking chain guiding technology, a Cypher query statement is automatically generated and corrected in two stages, and the problem that the query intention of a user and knowledge graph data are difficult to accurately align when the Cypher query is generated due to the diversity of query requirements of the user and a semantic gap between the knowledge graphs is solved.

The invention relates to a multi-mode large language model fine tuning method, system and device and a medium, and belongs to the technical field of artificial intelligence and computer vision crossing. The fine tuning method comprises the steps that an original business scene image is acquired and preprocessed, and a preprocessed image is obtained; performing bounding box coordinate labeling and semantic label definition on the entity target in the preprocessed image through a labeling tool, and outputting a structured labeling file; based on the preprocessed image and the structured annotation file, constructing a training sample set comprising multiple rounds of image-text dialogues; loading the pre-trained multi-modal large language model, configuring low-rank matrix decomposition parameters, and generating a fine tuning instruction set; and inputting the training sample set into a pre-trained multi-modal large language model, carrying out joint training operation based on the fine tuning instruction set, and outputting the fine-tuned multi-modal large language model. According to the method, the identification accuracy, the interaction capability and the system availability of the visual question-answering system in an actual application scene are improved.

The present application relates to a question answering interaction method and apparatus based on a large language model data vectorization query, and an electronic device and a computer-readable medium. The method comprises: performing semantic recognition on input data of a user, and determining a question category corresponding to the input data; determining a target vector database on the basis of the question category; converting the input data into an input vector; using the input vector to perform a vector query in the target vector database, and performing splicing to generate a query result; generating a prompt question by means of the input data and the query result; inputting the prompt question into a large language model, so as to acquire a logical analysis result; and when the logical analysis result is a positive result, performing question answering interaction on the basis of the logical analysis result. The present application can provide an intelligent question answering method for a user, thereby improving the answering quality and accuracy of a man-machine intelligent dialogue and question query.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a set of large language models to determine a natural language response to a query. One of the methods includes receiving a query related to a document. The document is submitted to a first model along with a prompt to generate an outline of the document. The document is submitted to a second model along with a prompt to generate metadata of the document. At least a portion of the query, document metadata, and the document outline are submitted to a third model with a prompt to generate a natural language response to the query. A selected sentence from the natural language response is correlated to a document sentence. The natural language response is provided to the user with an indication that the selected sentence from the natural language response is correlated to the document sentence.

The invention provides a consensus decision question answering system based on multi-AI agent game, and relates to the technical field of artificial intelligence. The system comprises a multi-domain information aggregation module, an interaction effect deduction module, a strategy fusion calibration unit, a distributed behavior adaptive mechanism and an aggregation strategy discrimination module. The multi-domain information gathering module is used for unifying multi-source strategy information and environment situation data, the interaction effect deduction module is used for analyzing and quantifying the mutual influence relation of strategies between intelligent agents, and the strategy fusion calibration unit generates correction suggestions based on a game deduction and optimization method. The distributed behavior self-adaptive mechanism is used for locally and progressively executing a correction path in an intelligent agent; and the aggregation strategy judgment module dynamically evaluates the overall strategy state. The multi-agent consensus decision-making question-answering method realizes consensus decision-making question-answering of multiple agents in a complex environment, can effectively identify and correct non-collaborative strategy deviation, and improves the coordination, stability and immunity of a system.

The invention discloses a question and answer system based on a semantic vectorization knowledge graph and approximate nearest neighbor clustering, and the system comprises a data uptake and preprocessing module which is used for the input and preliminary processing of an unstructured text; the knowledge graph construction module is used for constructing a dynamic knowledge graph according to the primarily processed data; the knowledge graph enhancement module is used for expanding representation of entities in the knowledge graph by adopting an information enhancer and converting each entity node and relation node in the knowledge graph into a high-dimensional semantic vector by using an embedded model; during work, the general knowledge graph can convert structured and unstructured data into semantic vectors, the semantic vectors are stored locally in the form of the knowledge graph, and cross-domain knowledge accurate retrieval is supported in combination with a fine-grained tree pruning technology. The knowledge integration model is based on an approximate nearest neighbor clustering method, so that integrated output of a plurality of large language models can be realized, and the hit rate of answers with relatively high credibility is effectively improved.

The invention provides a method and device for scoring and questioning according to interview question answers and electronic equipment, and belongs to the technical field of artificial intelligence, in the method, multiple similarity measurement algorithms are integrated to achieve multi-angle quantitative evaluation of applicant answers, a large model scoring link based on deep learning is creatively added, and the evaluation efficiency is improved. Therefore, the language understanding depth which cannot be reached by pure mathematical calculation is compensated. According to the double-track scoring architecture, on one hand, the objectivity and the consistency of scoring are guaranteed by means of an accurate mathematical model; on the other hand, subjective understanding and interpretation of scoring are enhanced by means of a large model with high semantic analysis ability, the accuracy and comprehensiveness of evaluation are improved, the process is automatically achieved, efficiency is high, and the method can rapidly, accurately and comprehensively evaluate the applicant answers. In addition, the scoring reason and the question-asking problem given by the large model provide valuable information for mining the knowledge mastering condition of the applicant.

The invention discloses a lightweight incremental question answering system based on graph structure index and double-layer retrieval. Comprises: a graph-based incremental index module for segmenting an input document into text blocks, extracting entities and relationships through a large language model (LLM), and constructing a dynamic knowledge graph; the double-level retrieval module is used for mapping user query into a graph structure, adopting a double-strategy retrieval mechanism associated with local keyword matching and global theme and combining graph vectors for collaborative query; and the path constraint sub-graph generation module is used for generating an identifier sub-graph based on an n-hop reasoning path on the basis of the retrieved content, and performing deep semantic extension and answer generation. During work, through the modular and progressive collaborative design, the system realizes full-process optimization from data acquisition to knowledge representation and from query response to deep reasoning.

Existing question answering approaches have the disadvantages that they possess limited contextual understanding due to which the retrieval process they use is inefficient in nature. Embodiments disclosed herein provide a method and system for domain-driven knowledge augmented question answering. The system receives a raw corpus data as input, wherein the raw corpus data is a domain specific data. Further, an indexed corpus is generated from the raw corpus data, during which a document chunking approach is used. The indexed corpus is then used for processing received queries received, in order to generate response to the received user queries.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a silk path knowledge base intelligent question answering system based on a large language model, comprising a silk path data acquisition module used for acquiring multi-source silk path knowledge data; the silk path knowledge base construction module is used for processing the multi-source silk path knowledge data to construct a structured knowledge base and an unstructured knowledge base; the data storage system module is used for storing a structured knowledge base and an unstructured knowledge base and providing a structured retrieval interface and an unstructured retrieval interface; and the man-machine interaction module is used for identifying a user intention according to the user question and outputting an answer corresponding to the user question by adopting a corresponding answer generation mode based on the user intention. According to the method, efficient and accurate silk path knowledge questions and answers are realized by constructing the multi-modal knowledge base and optimizing a retrieval algorithm and a prompt engineering technology.

The invention relates to the technical field of artificial intelligence, and particularly discloses a policy and regulation intelligent question answering method and system based on a large language model, and the method comprises the steps: segmenting a policy and regulation text into text blocks through a recursive character text segmentation method, and constructing a knowledge base in combination with expert labeling and automatic information extraction; performing semantic embedding on the text block by utilizing a pre-trained Transform model, generating a high-dimensional vector representation, and storing the high-dimensional vector representation in a Chroma database; the user question is converted into an embedded vector, relevant laws and regulations are retrieved through a Chroma database, a large language model and a knowledge fusion attention generation module are combined, and text embedding and knowledge graph information are fused to generate an answer; and the user obtains answers through the intelligent question-answer interaction interface and can view the original texts and explanations of laws and regulations. The system comprises a data acquisition and preprocessing module, a semantic embedding and storage module, a retrieval and generation module and a user interaction module. According to the method, large-size regulation texts are effectively processed, and the retrieval efficiency and the answer quality are improved.

The invention discloses a document knowledge base LLM intelligent question and answer method, device and equipment and a storage medium. The method comprises the steps that dynamic partitioning is conducted on a to-be-processed document, and vectorization and index storage are conducted on knowledge blocks; vector retrieval and keyword retrieval are carried out according to the natural language question, a vector retrieval result and a keyword retrieval result are fused, an answer is obtained through LLM, and the answer is fed back to a user after being safely filtered; when it is detected that the to-be-processed document is updated, the vector library and the index are synchronously updated, and the cue word template and the partitioning strategy are periodically optimized, so that the problem of form picture information loss can be solved, the integrity of document information analysis is guaranteed, the situation that the partitioning strategy is single is avoided, the multi-hop recall rate is increased, and the problem of semantic missing is avoided; a partitioning mechanism is reasonable, retrieval precision is improved, updating cost is reduced, data security is improved, implementation is convenient, universality is good, and the speed and efficiency of LLM intelligent question answering of the document knowledge base are improved.

A method of question answering using enhanced retrieval-augmented generation according to an embodiment includes receiving, by a computing system, a user query, pre-processing, by the computing system, the user query to determine whether the user query is associated with malicious intent, retrieving, by the computing system, relevant data from a knowledge base by using a keyword index and a semantic index in response to determining that the user query is not associated with malicious intent, prompting, by the computing system, a large language model to generate an answer to the user query based on only the relevant data retrieved from the knowledge base, and receiving, by the computing system, the answer to the user query from the large language model in response to the prompt.

The invention provides a complex question and answer method and system based on adaptive task deconstruction and multi-modal evidence aggregation, belongs to the technical field of natural language processing, and designs a dynamic Few-shot prompt construction method based on dependency syntax fingerprints to ensure that a prompt template is matched with a question structure; the invention discloses a dynamic problem deconstruction method based on confidence evaluation and auto-reflection. The method comprises the following steps: recursively decomposing a problem tree by using a large language model; converting the problem tree into a standardized linear task execution sequence by a problem tree context dependence specification and task sequence generation method; obtaining a high-correlation evidence set of each task based on an evidence generation method of two-way recall and cross encoder rearrangement; and the task sequence is reasoned and dynamically optimized by a question answer extraction method based on double-strategy aggregation reasoning. According to the method, the accurate complex question and answer result can be provided on the premise of ensuring the question disassembling quality, restraining error propagation and comprehensively recalling evidences.

The invention relates to a construction method and system of an intelligent question-answering system based on a planning knowledge graph library. The method comprises the following steps: acquiring multi-source space planning data in real time; carrying out fusion on the planning data; vectorizing the fused planning data; a plurality of preset retrieval enhancement models are modularized, and interaction of vectorized planning data among the modules is realized through a message queue; through a natural language processing method and a message queue method, constructing a plurality of knowledge maps which can be accessed by the preset retrieval enhancement model; training a pre-training model through the knowledge graph; in response to a user question, retrieving an enhancement model through dynamic routing selection, and retrieving entities and relationships in the knowledge graph; and based on the retrieval result, generating a questioning result through a pre-training model. According to the method, intelligent question answering, scheme generation and evaluation of space planning are realized through the knowledge graph and the pre-training model constructed by the multi-source data, the working efficiency of the space planning is improved, and the manual workload and the error rate are reduced.