468 results about "Knowledge question" patented technology

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

The invention discloses a method and system for generating enhanced knowledge questions and answers for heterogeneous database mixed retrieval, and the method comprises the following steps: (1) processing text data, and constructing a heterogeneous database; (2) receiving user query, and performing mixed retrieval in the heterogeneous database; the retrieval results are merged, and documents and triple information related to user query are obtained; (3) reordering the retrieved documents and triple information by using a reordering model, and selecting K retrieval results with the highest relevancy with user query; and (4) combining user query and retrieval results, constructing cue words, inputting the cue words into the large language model, and generating coherent and accurate natural language answers. By means of the method and device, more comprehensive and accurate information retrieval can be achieved, and the accuracy and reliability of knowledge question and answer results are improved.

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention provides a semantic element-based knowledge question and answer accuracy improving method, which comprises the following steps of: extracting information from text data, and constructing semantic nodes; word segmentation processing is performed on a question text input by a user, a word sequence is obtained, and semantic element representation of a question is formed; in the knowledge recall stage, a golden investment algorithm is applied to achieve knowledge retrieval; matching the candidate answers with semantic elements of the user question, and scoring and sorting according to keywords; and according to the query intention of the user question and the sorted candidate answers, constructing cue words of the large model, calling the large model to generate a final answer, and presenting the output answer according to a specified format. By integrating semantic analysis, knowledge recall, natural language processing and other technologies, the intellectualization level and response accuracy of the knowledge question-answering system are remarkably improved, and higher-quality service is provided for users.

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 provides a document knowledge questioning and answering method, a document knowledge storage method and a document knowledge questioning and answering device, and relates to the technical field of intelligent questioning and answering. All layers of nodes of a knowledge tree are traversed according to a target retrieval mode on the basis of the query statement to obtain a query result, the knowledge tree comprises multiple layers of nodes, the multiple layers of nodes at least comprise first-layer nodes, second-layer nodes and third-layer nodes, semantics of the first-layer nodes are semantics of text contents of knowledge point levels in the document, and semantics of the third-layer nodes are semantics of text contents of knowledge point levels in the document; the semantics of the second-layer nodes are the semantics of the paragraph-level text content in the document, and the semantics of the third-layer nodes are the semantics of the full-text-level text content in the document; and outputting an answer text corresponding to the query statement based on the query result. The method provided by the invention not only improves the recall rate of knowledge and the correlation of answers, but also enhances the overall experience of the user, so that the user can obtain more comprehensive and accurate answers in complex queries.

The invention discloses a knowledge graph-driven water conservancy intelligent question and answer interaction platform and method, and relates to the related field of data processing, and the platform comprises an entity disambiguation module which is used for constructing a water conservancy knowledge graph to carry out standardized modeling, generating structured knowledge nodes to carry out entity disambiguation on water conservancy objects, and generating entity identifiers; the full-text retrieval module is used for performing semantic analysis on a natural language query text of the user according to the entity identifier, identifying a query intention type, performing full-text retrieval and generating a multi-modal answer; and the iterative interaction module is used for performing dynamic questioning based on the multi-modal answers, generating user semantic feedback information, optimizing a query path according to the user semantic feedback information, and realizing iterative interaction of water conservancy knowledge questions and answers. The technical problem that existing water conservancy intelligent question and answer interaction is insufficient in question and answer accuracy and richness is solved, and the technical effect of improving the question and answer accuracy and richness is achieved.

The invention provides a multi-modal RAG knowledge question answering method and device applied to the vertical field, relates to the technical field of artificial intelligence, supports multi-modal input of to-be-answered texts including texts, pictures, audios and videos, and makes full use of semantic information of multi-modal data. And utilizing a plurality of preset retrieval models to retrieve the knowledge fragments corresponding to the to-be-answered text to obtain a plurality of retrieval results. Various retrieval results are fused through an RRF algorithm, and scores are calculated by using a target reordering model for reordering, so that the recall rate is improved. And based on the retrieval content and the preset knowledge frequency information, fine tuning sorting is further carried out, so that the method is more accurate and meets user expectation. According to the method, the natural language understanding ability of a large language model, a target reward model and related information recalled by the RAG are combined to generate optimal solution data, and meanwhile, a target scoring model and domain expert knowledge are utilized to provide more professional high-quality solution data meeting actual requirements.

The invention discloses a milk industry knowledge question-answering method and device based on a large model and RAG and a medium, and the method comprises the steps: collecting multi-source heterogeneous data of the milk industry, carrying out the term protection word segmentation processing of the multi-source heterogeneous data, and generating a labeled field corpus; based on a pre-training language model base, through a domain corpus injection and adversarial learning mechanism in a domain corpus, generating an enhanced language model adapted to the dairy industry terminology; receiving a milk industry problem of a user, executing semantic vector retrieval and term extension retrieval in parallel, and screening a multi-modal retrieval result through a dynamic sorting algorithm; and splicing the multi-modal retrieval result and the milk industry question into an enhanced prompt, inputting the enhanced prompt into an enhanced language model, generating a final answer corresponding to the milk industry question, and associating the final answer with a knowledge source.

The invention discloses a multi-modal knowledge question and answer retrieval method and system for a specific professional field, and belongs to the technical field of data processing. According to the method, the cosine similarity between the feature vectors is calculated in each mode through large model preprocessing to form the similarity matrix, and direct vector calculation of the problem and the knowledge base text in the professional field is realized by adopting a vectorization processing mode which is the same as that of the knowledge base text. And according to an input problem vector, through vectorization index retrieval in the professional domain knowledge base, selecting a plurality of text vectors closest to the problem vector from the professional domain knowledge base to obtain a plurality of pieces of knowledge related to the problem and the source of each piece of knowledge. According to the method, the knowledge question and answer reply accuracy of the target field is effectively improved, the knowledge question and answer reply integrity of the target field is guaranteed, basis support is realized, local deployment is supported, the information security problem is solved, and an intelligent application scene of knowledge question and answer retrieval is realized.

The invention discloses a legal knowledge question-answering system constructed based on a large language model and a method thereof, and relates to the technical field of large language models and the field of legal application. The system comprises a law and regulation knowledge base construction module, an RAG retrieval module, an LLM enhancement module and a user interaction interface. Based on an RAG retrieval enhancement generation related technology, the invention aims to perform efficient knowledge management on massive laws and regulations and recall related legal knowledge through accurate knowledge retrieval so as to enhance the effect and accuracy of an LLM large-scale language model in providing legal consultation service; by combining the RAG technology and the LLM technology, efficient knowledge management and accurate retrieval of massive laws and regulations are achieved, and an innovative and efficient solution is provided for the field of legal consultation services; according to the technology, developers do not need to re-train the whole LLM for each specific task, and only need to provide additional input for the LLM by connecting a related knowledge base, so that the accuracy of answers is improved.

The invention relates to the technical field of knowledge questions and answers, in particular to a method and system for detecting and repairing large language model output illusion, which comprises the following steps of: dividing answer content into a plurality of sentences, calculating the confidence coefficient of each sentence, judging whether the confidence coefficient of each sentence exceeds a set threshold value or not, performing fact extraction on the sentences with the confidence coefficient lower than the set threshold value, obtaining triple data of each sentence; generating a subgraph A of each sentence based on the triple data of each sentence; extracting a sub-graph B from the local knowledge graph based on the subject in the triple data of each sentence; calculating the similarity between the sub-graph A and the sub-graph B, and if the similarity is smaller than a set threshold value, repairing the sub-graph A according to the sub-graph B to obtain a repaired sub-graph A; according to the triad data corresponding to the repaired subgraph A, obtaining a repaired sentence; and after all the sentences with the confidence lower than a set threshold value are repaired, final answer content is obtained. And the accuracy and reliability of model output are improved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

The invention provides a railway knowledge question answering method and device based on a natural language model, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a railway business question; inputting the railway business question into a pre-trained railway natural language large model to obtain an answer corresponding to the railway business question; wherein the railway natural language large model is obtained by pre-training a natural language large model based on a railway natural language sample library and performing supervised fine tuning on the pre-trained natural language large model; the natural language sample library is formed by labeling data in a railway natural language knowledge base; the railway natural language knowledge base is formed by classifying and storing railway business data. Through the method provided by the invention, the railway natural language large model with professional knowledge question-answering ability is trained and generated, and the efficiency and accuracy of railway knowledge question-answering are improved; and related problems of text application scenes in the railway field are solved.

The invention discloses a large language model retrieval enhancement generation method oriented to cyberspace security emergency intelligent analysis. The retrieval and generation capability of a large language model in knowledge questions and answers in professional fields is enhanced through a knowledge graph. The method comprises a knowledge base construction module and a knowledge enhancement response generation module. The method mainly comprises the following steps: collecting multi-source data and preprocessing to construct a basic data set; defining an entity type and a relationship type, and performing entity recognition and relationship extraction based on a large language model to construct a knowledge graph; designing a graph-based hierarchical retrieval strategy to perform knowledge retrieval; and generating professional answers through a knowledge enhancement generation framework. According to the method, the accuracy of knowledge questions and answers in the professional field is remarkably improved, the accuracy of the professional field in experimental evaluation reaches 86.7%, the coverage rate of retrieval knowledge reaches 81.2%, and the interpretation quality score is 4.35 score. According to the method, the limitation of a traditional large language model in professional field application is overcome, and good expandability and adaptability are achieved.

The invention discloses a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention discloses a knowledge graph-based knowledge question and answer method and device and a storage medium, and belongs to the technical field of natural language processing. The method comprises the following steps: processing an obtained question statement input by a user to obtain a subject entity and a target question embedding vector; mapping the subject entities to the obtained target knowledge graph and the embedded representation thereof, and generating candidate answer entities according to the subject entities and the proximity relation thereof; performing similarity calculation on the subject entity embedding vector, the candidate answer entity embedding vector and the target question embedding vector to obtain a target candidate answer entity set; extracting a relation path set from the subject entity to the target candidate answer entity from the target knowledge graph, and obtaining a target relation path embedding vector based on the relation path set; and performing similarity calculation on the target question embedding vector and the target relation path embedding vector to complete answer reasoning. According to the method, the accuracy and efficiency of knowledge questions and answers are improved by combining a multi-hop reasoning technology.

The invention provides an answer generation method, a knowledge question-answering system and electronic equipment, and belongs to the technical field of artificial intelligence. According to the method, through multi-dimensional feature extraction, features in different dimensions can be extracted for subsequent complexity classification judgment. The complexity of subsequent processing can be reduced by screening out the core feature which can best reflect the complexity of the problem from the multi-dimensional feature set. And aggregating each core feature in the core feature set by using an adaptive aggregation strategy to obtain an aggregated feature vector, inputting the aggregated feature vector into a hierarchical complexity shunt, and for a query problem with a lower complexity level, directly outputting the complexity level by a lightweight classification layer. And for the query problem with a relatively high complexity level, the complexity level is output by the deep classification layer, so that the accuracy and efficiency of complexity classification can be ensured. And finally, according to the complexity level corresponding to the query question, a corresponding retrieval and answer generation strategy is selected, so that the flexibility and efficiency of answer generation can be improved.

The invention relates to the technical field of large model knowledge questions and answers, and discloses an enterprise knowledge question and answer system based on a large language model, which comprises a heterogeneous semantic embedding module, a permission mask calculation module, an enterprise term alignment module, a candidate knowledge query module and a question and answer generation output module, in the prior art, an enterprise question and answer mode is realized only depending on keyword matching or a fixed FAQ rule, and particularly under the conditions of enterprise internal knowledge distribution fragmentation, authority differentiation and term ambiguity, accurate, compliant and context-consistent semantic question and answer cannot be realized. Due to the fact that multi-mode unified embedding, the semantic level authority control mechanism, the term graph alignment model and the RAG enhanced question and answer mechanism are introduced, cross-format and multi-department enterprise intelligent question and answer is achieved, the problems of semantic drift and unauthorized leakage are avoided, and the accuracy and safety of the question and answer system are improved.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project knowledge question and answer construction method and system based on a large model, and the method comprises the following steps: knowledge preparation, continuous evaluation optimization, user query processing, answer retrieval recall, and content generation. The hydraulic engineering knowledge question and answer construction method and system based on the large model have the beneficial effects that the large model and traditional knowledge of the hydraulic engineering industry are fused, so that the generated content is higher in quality and safer, and the problems of illusion and safety of LLM generation content of the large model are relieved; and a continuous evaluation index of the water conservancy field knowledge document system is provided, so that the system can answer questions with high quality.

The invention discloses a dialogue type enterprise knowledge question answering method and device, a storage medium and electronic equipment, and relates to the technical field of data analysis, and the method comprises the steps: obtaining user request information; obtaining a corresponding channel; reconstructing the question content to obtain the reconstructed question content; performing vectorization processing on the reconstructed question content to obtain vectorization information of the reconstructed question content; obtaining a plurality of first FAQ knowledge and second FAQ knowledge; obtaining a plurality of pieces of first document block knowledge and second document block knowledge, and obtaining documents corresponding to a plurality of document blocks; obtaining a corresponding prompt word template based on the dialogue intention and the channel; and constructing a large language model, and sorting to obtain corresponding answer information. Based on the question content of the user, the dialogue intention is obtained through analysis, the knowledge base is matched, the answer information is obtained through arrangement, the user question is automatically answered, and the method has the advantages of being simple, efficient and high in real-time performance.

The invention discloses an electric power knowledge question-answering system construction method and system combined with engineering ethical analysis, and the construction method comprises the steps: constructing all modules, and carrying out the fine adjustment of a large language model based on a P-Tuning method, so as to construct an electric power system question-answering knowledge base; the question understanding and classification step is responsible for receiving input questions of a user and performing semantic analysis, technical classification and ethical risk identification on the input questions; searching background knowledge; carrying out engineering ethical analysis; generating questions and answers; and a feedback and improvement step: entering a model tuning stage, and carrying out adaptive tuning on the model by adopting a reinforcement learning framework. According to the method, comprehensive engineering ethical analysis can be carried out on technical schemes and operation suggestions, finally, answers generated by the system are accurate and feasible in technology and have ethical compliance at the same time, it is ensured that a user can balance technical requirements and ethical responsibility in practical application, the intelligent level of an electric power knowledge question-answering system is remarkably improved, and the user experience is improved. And important support is provided for safe operation and sustainable development of the power industry.

The invention provides a method, system and device for achieving industry knowledge questions and answers based on a large model and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that standard answers of industry experts are collected for questions with the occurrence frequency higher than a threshold value in a target industry, and a standard answer library is constructed; collecting professional knowledge related to the target industry, and constructing an industry knowledge base; responding to a target question, input by a user, of a target industry on a front-end interface, matching a standard answer library and a retrieval industry knowledge library according to the target question, when a return result exists, constructing a cue word according to the return result, and when the return result does not exist, constructing the cue word according to the retrieval industry knowledge library; according to historical multi-round dialogue information of the user on the front-end interface, cue words are constructed and provided for the general large model; and returning a knowledge question and answer result output by the general large model to the user through a front-end interface. By constructing the standard answer library and the industry knowledge library and combining the general large model, intelligent answering of the industry questions is achieved, and the accuracy and specialty of question answering are improved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention relates to the field of knowledge questioning and answering, and particularly discloses a knowledge questioning and answering method and system based on the artificial intelligence technology and electronic device.Firstly, to-be-recognized audio collected by a sound sensor and an oscillogram extracted from the to-be-recognized audio collected by the sound sensor are obtained; and then feature extraction and correlation analysis are performed on the two, and finally a classification result is obtained through a classifier to judge whether the to-be-identified audio is the human voice audio needing knowledge questioning and answering, so that it is ensured that the system only processes the human voice audio needing knowledge questioning and answering, and the performance and user experience of the knowledge questioning and answering system are improved.

The invention discloses an advertisement compliance knowledge question-answering method, device and equipment for foreign enterprises, and the method comprises the steps: obtaining advertisement compliance data of a global data source, carrying out the document structuring and knowledge extraction of the advertisement compliance data, and constructing an advertisement compliance knowledge graph; converting text data in the advertisement compliance knowledge graph into vectors to obtain vector data; semantic analysis is conducted on a query question input by a user, construction is conducted in combination with the vector data, prompt words are obtained, the prompt words are input into a preset large language model, and target answers are obtained; and outputting the target answer based on the advertisement compliance knowledge graph, and visually displaying the target answer in a corresponding answer query graph. And comprehensive and efficient compliance information query and answer services can be provided, so that compliance review requirements of foreign enterprises on advertisements in a multi-language and cross-regional environment can be met.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

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 question and answer confrontation detection method and a knowledge question and answer confrontation detection system for a retrieval-enhanced large language model. The method comprises the following steps: firstly, receiving a user question, carrying out semantic analysis, extracting key features, retrieving and extracting a related context in a local knowledge base by utilizing the features, and inputting the question and the context into a large language model to generate a basic answer; and performing semantic replacement on the questions and the contexts to generate a plurality of expression forms. And randomly sampling expressions before and after replacement according to questions and contexts, and inputting the expressions into a large language model to generate answers. And calculating a semantic distance between the answer generated by the sampling combination and the basic answer, and inputting the semantic distance into the unsupervised outlier detection model as a feature value. If the feature value is judged to be an outlier by the detection model, the adversarial risk exists, and otherwise, a basic answer is returned. According to the method, semantic replacement and unsupervised adversarial detection are carried out on the cue words and the contexts, adversarial attacks aiming at questions and local knowledge bases are resisted, and the accuracy and robustness of a large language model are improved.

The invention provides a question and answer model training method and device and an intelligent question and answer method and device for a specific field, and relates to technologies such as large models, model training and deep learning in the field of artificial intelligence and the technical fields such as intelligent search and intelligent question and answer. According to the specific implementation scheme, corpus data of a specific field are obtained; meta-knowledge question and answer data are constructed based on the corpus data through a first model, and the meta-knowledge question and answer data comprise meta-questions and answers corresponding to the meta-questions; based on the meta-knowledge question-answer data, domain knowledge training is carried out on the to-be-trained question-answer model; on the basis of the meta-knowledge question and answer data, a semantic-related question set is constructed for the meta-questions, and structured reasoning training data is generated through a second model on the basis of the question set of the meta-questions; and based on the structured reasoning training data, performing reasoning ability enhancement training on the question and answer model to be trained after domain knowledge training. The question and answer performance of the model in the vertical field can be remarkably improved.