1070 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 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 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 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 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 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 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 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 provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention discloses a knowledge-based visual question-answering method and device based on big and small model collaboration and a medium, belongs to the technical field of visual question-answering, and solves the problem of how to improve the accuracy of complex questions faced by a visual question-answering technology. In the image description extraction step, an image description and key object label set which is highly associated with a natural language problem is generated, and in the entity enhancement processing step, key entities are extracted and a clarity problem and an entity example which are used for resolving semantic ambiguity are generated. In the candidate answer generation step, a plurality of semantically complementary answers are generated under different reasoning dimensions on the basis of a multi-round generation strategy, optimized candidate answers are screened on the basis of a model built-in scoring mechanism, in the context example retrieval step, examples most related to current input are retrieved, and finally, the candidate answers are obtained. And the large language model receives a unified input prompt formed by splicing output results in the steps, and inference is performed in an autoregression mode to generate answers, so that the accuracy of the visual question-answering technology facing complex questions is effectively improved.

The invention relates to the technical field of large language models, and discloses a multi-path legal inference engine based on an MCP protocol, which comprises a legal question input module, a legal question answering module and a question and answer result output module, in combination with a Monte Carlo tree search algorithm, a multi-path deduction mechanism, a retrieval enhancement mechanism and a user feedback optimization mechanism, an MCP unified management model internal state and knowledge activation and reasoning path information are relied on, and interpretable legal answers corresponding to legal query questions input by a user are generated. By means of key information flow integrated by the MCP protocol, answers, reasoning chains, reference bases and credibility evaluation are output in a visual mode, and the professionality, the interpretability and the user credibility of the whole legal reasoning process are remarkably improved.

The invention discloses a dynamic feature retrieval generation and management method based on a cross attention mechanism, which belongs to the technical field of natural language processing, and comprises the following steps: step 1, converting knowledge base document fragments into atomic knowledge units, each atomic knowledge unit comprising question and answer pairs, codes as key value pairs, and adding dynamic priority weights; self-attention query is replaced with a double-channel query structure, one channel is used for cross attention, a cross attention query vector is generated through linear transformation, and key value pairs of atomic knowledge units are used; and step 3, training a cross attention adapter, freezing the weight of the language model, optimizing parameters of the adapter, and dynamically adjusting a loss function by using pre-judgment parameters based on input sequence context complexity. By means of the method, deep correlation description of user input and knowledge fragments can be achieved, semantic ambiguity is eliminated, knowledge injection and context information are balanced, distortion is avoided, and the strict requirement for semantic precision in the professional field is met.

The invention discloses a medical knowledge question-answering system based on an RAG (Retrieval-Augmented Generation) architecture, relates to the field of artificial intelligence and natural language processing, and specifically comprises an input representation module, a knowledge retrieval module, a context construction module and a generation reasoning module. The input representation module is used for encoding user query and medical knowledge entries into high-dimensional dense vectors and comprises a semantic encoder; the knowledge retrieval module comprises a sparse retrieval unit, a dense retrieval unit and a fusion sequencing unit; the context construction module comprises a feature splicing unit and a code fusion unit; and the generation reasoning module calls a large language model based on the fused context to generate medical question and answer content in a natural language form. According to the system, by introducing the structured medical knowledge base and a mixed retrieval mechanism, the accuracy and specialty of questions and answers are effectively improved, the language model illusion phenomenon is reduced, the knowledge updating capacity is enhanced, and the system is suitable for application scenes such as clinical consultation and intelligent medical questions and answers.

The invention provides an intelligent question and answer method and device based on large model multi-source information fusion and electronic equipment, relates to the technical field of computers, in particular to the fields of big data, intelligent search and the like, and can be used for application scenes such as intelligent question and answer. According to the specific implementation scheme, pre-retrieval is conducted from a data table structure information base and a data table field information base according to a user problem, and a pre-retrieval result is obtained; retrieving from a business knowledge base, a business rule base and a question number template base according to the user question to obtain a knowledge enhancement result; according to the pre-retrieval result and the knowledge enhancement result, utilizing a query instruction generation model to obtain a query statement; and querying from the data table set according to the query instruction to generate an answer corresponding to the query word. The scheme can improve the professionality of intelligent questioning and answering, and is suitable for application scenes of various industries.

The invention belongs to the related technical field of product detection, provides a multi-view zero sample anomaly detection method and system based on cross-modal prompt reasoning, and aims at solving the problem of multi-view zero sample anomaly detection by constructing core technologies such as multi-view pose estimation and alignment, static-dynamic prompt collaboration, vision-language progressive fusion, feature space semantic enhancement and the like. And a set of end-to-end anomaly detection and reasoning system is formed. Particularly, a collaborative mechanism of a dynamic learnable prompt pool and a static attribute prompt library is designed, deep fusion of prompts is realized through cross attention, and multi-view feature compression and semantic decoding are performed by adopting a visual angle self-adaptive hybrid expert model. Zero sample anomaly detection and visual question and answer performance is further improved on multiple industrial public data sets, and the method can be widely applied to industrial precision part quality inspection, intelligent manufacturing and other complex scenes needing high-precision and multi-view perception and semantic reasoning.

The invention discloses a question answering method based on structured document retrieval enhancement, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing structured information analysis and blocking processing on a structured document to construct a tree structure reflecting a hierarchical relationship of the document; based on a cross-document association relationship, combining the tree structures corresponding to the plurality of structured documents to form a multi-document tree structure network; and when a question request is received, generating corresponding retrieval enhancement information through the tree structure network, and inputting the retrieval enhancement information into the question and answer model to obtain answer information matched with the question request. According to the method, the multi-document tree structure network which takes the tree hierarchical relationship of the structured document as a core and fuses cross-document topic clustering and reference mapping is constructed, so that efficient retrieval enhanced questions and answers oriented to the structured document are realized, and the accuracy, context consistency and traceability of answers are remarkably improved.

The invention discloses an energy field intelligent knowledge base question answering system based on multi-model cooperation, and aims to solve the problems of data type identification, image structured extraction and cross-modal indexing in energy field multi-modal document question answering. The system comprises 13 core modules, dynamic cooperation of a multi-mode large model and a large language model is achieved through a collaborative scheduling module, data types involved in user problems can be recognized, and corresponding modules can be called; structured information of images such as charts and flow charts can be extracted from documents and coded; and cross-modal retrieval is realized through a unified semantic vector. When answers are integrated, an attached source is quoted, and traceability is ensured. The system improves the accuracy and efficiency of complex document question answering in the energy field, and is suitable for professional document question answering scenes containing multiple types of images.

The invention discloses a medical intelligent question and answer method and system based on multi-modal data, a medium and a product, and relates to the field of medical intelligent question and answer. Performing knowledge retrieval in a preset vector database according to the query content to obtain a knowledge retrieval result; performing query intention recognition on the query content to obtain a query intention recognition result; generating a query answer according to the knowledge retrieval result and the query intention recognition result; wherein the query answer comprises one or more of text, image annotation, voice synthesis and video interpretation. The system supports various output forms such as text, image annotation, voice synthesis and video interpretation, explains and analyzes the illness state or medical diagnosis and treatment results of the patient from multiple dimensions, provides rich and visual diagnosis and treatment reports and interactive interfaces for doctors and patients, and effectively improves the user experience and decision-making efficiency.

The embodiment of the invention provides a question and answer management method and device based on a large model, a storage medium and a program product. In the scheme, a'short-term first and long-term 'progressive recall strategy is introduced, that is, in a storage stage, session abstracts are generated in a segmented manner under the condition that whether the collection frequency of a preset round and the topic category are changed or not, the session abstracts are sequentially written into a short-term memory storage area and sink to a long-term memory storage area after the preset storage duration is reached, and a subsequent recall path is pre-buried; in the acquisition stage, the latest abstract closest to the current round is recalled from the short-term region, and if the abstract is missing, the cross-session or cross-round historical session abstract continues to be complemented from the long-term region. According to the mechanism, session context management of'instant light and thin 'and'long-term consistent' is considered under the condition that the computing power and the bandwidth cost are not remarkably increased, accurate contexts are provided for complex session scenes with long-period, multi-topic and multi-file cooperation, and a large question and answer model can make more accurate and more consistent questions and answers based on complete and related context information.

The invention relates to the technical field of intelligent question answering, in particular to an intelligent question answering implementation method and system based on an MCP protocol. The intelligent question and answer implementation method based on the MCP protocol comprises the following steps: receiving and analyzing an original request, actively sensing related environment information, integrating and constructing a context background, and drawing core association between indexes and dimensions and specific calling logic of a tool; in combination with recorded information of the tool registration center, a tool combination is selected, and a task execution plan is made; and scheduling and calling a tool function, performing integration, verification and deep reasoning on a returned result, and finally extracting key information related to the core demand of the user, and presenting the key information to the user. According to the intelligent question and answer implementation method and system based on the MCP protocol, real-time acquisition and fusion of multi-dimensional information are realized through a dynamic function calling mechanism, so that the intelligent question and answer system has the capabilities of environment perception, multi-round reasoning and cross-modal decision making, and the question and answer accuracy and timeliness in a complex scene are remarkably improved.

Systems and methods are provided for a natural language question answering service to provide answers to natural language questions regarding network-based services or computing domains. The natural language question answering service may receive the natural language question from a customer computing device. An aggregator of the natural language question answering service can retrieve passages from search systems based on the question and generate a prompt. A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer. The answer may be verified by a verifier of the natural language question answering service. Attribution may be applied to the answers and retrieved passages to produce references, inline citations, and similar questions. A watermarking module of the natural language question answering service may watermark the answer if it is verified.

Embodiments of the present disclosure provide a question answering method and apparatus, a computing device, a storage medium, and a computer program product. The question answering method comprises: determining a target question, a question answering field to which the target question belongs, a vector library corresponding to the question answering field, and a text library corresponding to the vector library; determining a target vector corresponding to the target question, determining, from the vector library, a template vector corresponding to the target vector, performing word segmentation on the target question, and determining, from the text library, a template text corresponding to a word segmentation result; on the basis of the template vector, the template text, the target vector and the target question, determining an initial answer corresponding to the target vector; when it is determined that a similarity result between the target vector and the initial answer satisfies a preset similarity condition, determining a first prompt text corresponding to the target vector on the basis of the initial answer; and using a language model and the prompt text to determine a target answer corresponding to the target question. The method improves the accuracy of determined target answers corresponding to target questions.

The invention relates to an enhanced question answering method based on agricultural large model training and RAG, and belongs to the field of agricultural large data, the method comprises the following steps: collecting agricultural data, and constructing an agricultural large model training data set; the method comprises the following steps of: constructing input embedding of a Transform and a Transform encoder to train a large model based on a Transform framework; after a user inputs a question text, the large model sequentially executes dynamic word embedding disambiguation, regional term replacement, entity perception position coding and generation of enhanced representation of an input sequence, then, a Transformer encoder extracts deep semantic features, a classification task directly outputs prediction labels, and a generation task generates answers word by word through a decoder; and performing RAG enhanced questioning and answering in combination with a retrieval enhanced generation mechanism. According to the dynamic word embedding mechanism, the ambiguity problem of agricultural terms in different contexts and regions is effectively solved, and the accuracy of agricultural semantic understanding is remarkably improved.

The invention relates to the technical field of data analysis, and discloses a feedback generation method and device based on a business knowledge graph, equipment and a medium, and the method comprises the steps: collecting multi-source business data, cleaning and segmenting the multi-source business data to form standardized text fragments, and carrying out the entity recognition and relation extraction of the text fragments to construct the business knowledge graph; receiving a natural language query instruction, extracting query intention features and key entity references, mapping the key entity references into the business knowledge graph, and performing association traversal under the guidance of the query intention features to obtain associated knowledge sub-graphs; and serializing the associated knowledge sub-graph into a knowledge background context, and jointly inputting the knowledge background context and a natural language query instruction into a pre-training language generation model to generate natural language feedback information. The method can be applied to business scenes such as financial science and technology, medical health and the like, semantic structuring is carried out on text knowledge, reasoning is carried out in knowledge association in combination with query semantics, accurate question answering is achieved, and knowledge retrieval efficiency is improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering system implementation method and system.The intelligent questioning and answering system implementation method comprises the following steps of document collection and preprocessing, document dicing, document layout analysis and table layout analysis; the method has the beneficial effects that a semantic association network and a multi-modal index system are formed through offline document collection, preprocessing, slicing, layout analysis, data extraction and knowledge graph construction; in the online part, the capabilities of Embedding vectorization, multi-index joint retrieval, tensor reordering, AI database integration and large language model generation are combined, accurate semantic understanding and rapid knowledge matching of user questions are realized, high-quality answers are generated, and interactive feedback optimization is supported.

The invention discloses a knowledge destruction attack method and device based on an RAG system and a medium, and relates to the technical field of internet security, and the method comprises the steps: inputting a target question and an error answer into the RAG system, and generating an initial confrontation text; performing multiple rounds of iterative optimization processing on the initial adversarial text to obtain a target adversarial text; inputting the target adversarial text into a knowledge base corresponding to the RAG system; the RAG system responds to a question demand input by a user, and retrieves and outputs a question answer corresponding to the question demand from the knowledge base; and inputting the question demand and the question answer into a large language model, so that the large language model outputs a wrong answer corresponding to the target question. The method and the device are used for solving the problems of low output result precision, poor attack effectiveness and low concealment when knowledge destruction attack is carried out based on an RAG system in the prior art, and the precision of the output result is improved under the condition that the knowledge destruction attack is effectively carried out with high concealment.