455 results about "Question Text" patented technology

The invention provides a question and answer method based on a thinking chain and an intelligent agent and a ChatBI system. The method comprises the steps that a question text input by a user is received, the intelligent agent is used for analyzing and planning the question text, a thinking chain mechanism is used for decomposing the question text into a plurality of subtasks, and the execution sequence of the subtasks is determined. The whole question and answer process is gradually disassembled into a plurality of subtasks through a thinking chain mechanism, and each subtask is sequentially executed according to the execution sequence, so that the accuracy and the flexibility of query are improved. And for the execution process of each sub-task, in response to determining that the execution of the sub-task fails, re-planning the execution sequence of the plurality of sub-tasks until the plurality of sub-tasks are executed in sequence, and obtaining a question and answer result corresponding to the question text. In the execution process of the subtasks, the agent dynamically optimizes the question and answer process by evaluating the execution results of the subtasks in real time, the limitation of static query is avoided, and the question and answer efficiency and the accuracy of question and answer results are improved.

The present application relates to a retrieval model training method and apparatus and a computer device. The method comprises: acquiring a knowledge document, and performing text segmentation processing on the knowledge document to obtain a text block set (501); on the basis of the text block set and summary information comprised in each text block, writing a question for each text block to obtain one or more question texts corresponding to each text block, separately combining the one or more question texts corresponding to each text block with the corresponding text block to obtain one or more data pairs corresponding to each text block and, on the basis of the one or more data pairs corresponding to each text block, generating a data pair set (502); and using the data pair set to train a first retrieval model so as to obtain a second retrieval model (503).

The present invention provides an output detection method for a large language model, comprising: acquiring an output text of a large language model for a questioning text, and acquiring a target knowledge graph of a field to which the questioning text belongs, the target knowledge graph comprising knowledge data in the field to which the questioning text belongs; performing knowledge extraction on the output text to obtain knowledge data to be detected of the output text; and by means of the target knowledge graph, detecting the knowledge data to be detected, so as to obtain a knowledge detection result of the output text. Knowledge extraction is performed on the output text of the large language model so as to obtain the knowledge data to be detected of the output text, and the target knowledge graph in the field is used to detect the knowledge data to be detected, such that whether there is a knowledge error in the output text can be determined, thus preventing the large language model from outputting incorrect knowledge to users.

The present invention relates to the related technical field of natural language inference. Disclosed is a large language model joint inference method based on a knowledge graph-enhanced chain-of-thought prompt, comprising: constructing a local knowledge subgraph; decomposing an original question text into S sub-question texts and concatenating the original question text and the S sub-question texts; inputting a concatenated question text into a graph inference model to obtain a weighted entity distribution; from the weighted entity distribution, extracting first G answer entities having the highest confidence level, using an inference transition matrix to trace an inference process of each answer entity, and generating an inference path from a question entity to the corresponding answer entity; and using the inference path to assist a large language model in predicting an answer to the original question text. By means of the method, the large language model can quickly and accurately find an answer to a question text.

The invention belongs to the technical field of artificial intelligence, and relates to an image-text question answering method, system and device based on multi-modal RAG and a storage medium, and the method comprises the following steps: 1) extracting multi-modal information from a PDF document, representing the multi-modal information as dense vectors, and storing the dense vectors in a text vector database and an image vector database; 2) obtaining a semantic embedding vector of the problem text and a multi-modal embedding vector of the problem text; obtaining a semantic embedding vector of the description text of the problem image and a multi-mode embedding vector of the problem image; 3) performing coarse screening in the text vector database and the image vector database by using the semantic embedding vector and the multi-modal embedding vector, finding out coarse screening text data and coarse screening image data, performing multi-modal fine ranking on the coarse screening text data and the coarse screening image data, and obtaining text data and image data which are retrieved and recalled; and 4) generating a final answer by the multi-modal large language model. According to the method, multi-modal data in a long document can be effectively analyzed, and information most relevant to a problem can be accurately retrieved.

The invention discloses a traffic question and answer method, device and equipment based on a large language model, and a medium. A traffic safety knowledge graph is constructed in advance. After the question text is divided into a plurality of sub-question texts, for the plurality of sub-question texts, determining a target entity with the highest similarity corresponding to the sub-question texts in the traffic safety knowledge graph; and determining a target sub-graph having a first-order relationship with the target entity. And for the multiple sub-question texts, inputting the sub-question texts, the entities and relationships in the corresponding target sub-graphs and the traffic long text data into a large language model, and determining sub-answer texts corresponding to the sub-question texts based on the large language model. And finally, inputting the question text, the plurality of sub-question texts and the corresponding sub-answer texts into a large language model, and determining a target answer text corresponding to the question text based on the large language model. And the accuracy of the determined target answer text is ensured.

The invention relates to the technical field of artificial intelligence, and provides a medical knowledge question answering method, system and device based on a large language model, and the method comprises the steps: obtaining a question text inputted by a user; calling a quick question and answer language model to retrieve answers from a vector database according to questions proposed by a user, vector data in the vector database being real number vectors formed according to text fragments related to the digestive specialized knowledge, if the answer is retrieved, filling the question text and the retrieved answer into a prompt template to generate a standardized answer text, and outputting the standardized answer text to the user; and if no answer is retrieved, calling a deep reasoning language model to call an execution tool related to a reasoning task corresponding to the current question according to the question to perform online answer search, filling a question text and a search result into a prompt template to generate a second standardized answer text, and outputting the second standardized answer text to the user. Through cooperative work of the two intelligent models, the accuracy and the reply speed of medical knowledge questions and answers are considered.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention discloses a large model retrieval enhancement generation method and system, and the method comprises the steps: obtaining the comprehensive document data of a power grid field, carrying out the partitioning processing of the comprehensive document data, and obtaining the partitioned text data; performing deep analysis on the block text data to generate a thinking chain enhanced text; encoding the thinking chain enhanced text based on a hierarchical weighted encoding method to generate a thinking chain enhanced semantic vector; inputting the block text data into a pre-trained large language model to obtain a generated task perception representation vector; splicing the thinking chain enhanced semantic vector and the generated task perception representation vector to obtain a fusion vector, and constructing a vector database in the power grid field based on the fusion vector; responding to the text retrieval signal, obtaining question text data of the target user, analyzing the question text data to obtain a question fusion vector, comparing the question fusion vector with a vector database, and obtaining retrieval data of the question text data based on a comparison result.

The invention relates to a construction method of a letter intelligent question-answering system based on self-adaptive knowledge graph enhanced LLM, and the method comprises the steps: obtaining the text data of a target region, carrying out the preprocessing of the text data, converting the text data into a vector format through a fine-tuning sentence converter, and constructing a vector database; vectorizing the user question text and the identity information, calculating the similarity of the user question text and the identity information with a vector database to generate context information, jointly inputting the context information and the user question into a generative large language model, and outputting a plurality of candidate answers; and based on a pre-constructed target domain knowledge graph, carrying out joint reasoning verification on the plurality of candidate answers, sorting the candidate answers through a scoring mechanism, and selecting a final answer. Complex questions can be answered, and the accuracy and reliability of questions and answers are improved; the answer can be accurately generated through reasoning without based on a preset answer template, and the accuracy and logicality of the answer are ensured.

The invention relates to the technical field of artificial intelligence, in particular to a process reward model training method and system. The method comprises the following steps: calculating a confidence score of a question text in each sample; based on the correctness score, the confidence score and the tolerance distance hyper-parameter of each reasoning step of the problem text in the labeled sample, obtaining a target correctness score of each reasoning step of the problem text in the labeled sample; and training the process reward model based on binary cross entropy loss between the correctness prediction score of each reasoning step of the problem text in the labeled sample and the target correctness score, and taking the trained process reward model as a target process reward model. According to the method, the accuracy and reliability of process reward model training are improved, and the text generation precision of a large language model is enhanced.

The invention provides an intelligent question and answer method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a question text input by a user; performing intention recognition processing on the question text to recognize an intention corresponding to the question text; performing classification processing on the intention to determine a retrieval type corresponding to the intention; and according to the intention, retrieving data matched with the intention in a database corresponding to the retrieval type, and generating an answer corresponding to the question text based on the retrieved data. According to the technical scheme, questions of the user can be replied in a multi-mode mode, on one hand, the user is helped to obtain more accurate and visual answers, and on the other hand, inconvenience brought by further retrieval of the user is avoided under the condition that the user only obtains the answers in the character mode but needs other answer modes.

The invention discloses a dialogue generation method and device, equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining multiple groups of official document question and answer pairs based on a corpus, and adding a quality score to an answer text in each group of official document question and answer pairs to obtain multiple groups of training question and answer pairs, the quality score is used for representing the quality of the content of the answer text; inputting the question text in each group of training question and answer pairs into a pre-trained big language model to obtain a model output text carrying a quality score, the pre-trained big language model being a big language model that has learned a natural language structure; the quality scores of the model output texts are input into a preset loss function to obtain loss values, model parameters of the large language model are updated based on the loss values to obtain an official document question and answer model, and the loss function is constructed based on a preference optimization model. According to the method, official document questions and answers can conform to values, intentions and preferences of human beings.

The invention provides an RAG method based on semantic precise blocking and precise background information generation, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in a preparation stage before reasoning, preprocessing a large number of related knowledge texts, and segmenting the texts into coarse-grained text blocks according to the number of words; dividing the coarse-grained text block into a plurality of fine-grained text blocks according to semantics through a large language model, and generating a context abstract and an association problem for each fine-grained text block; splicing the fine-grained text block and the context abstract into a final text block, and taking the final text block as a block containing accurate background information; and storing the final text blocks and association problems in different vector databases after vectorization. In the reasoning stage, when facing a user question, the system firstly vectorizes a user question text, then retrieves the most relevant items from the different vector databases for merging, and then optimizes and sorts the retrieved results through a reordering model. And selecting the top-ranked text blocks for the large language model to generate answers. According to the method, the problems of inaccurate blocking, context information missing and inaccurate retrieval in the traditional RAG method are effectively improved.

The embodiment of the invention provides a reply text generation method and device, a storage medium and a program product, and relates to the field of text generation, the reply text generation method comprises the steps that at least one reference question text is determined according to a target question text, and the target question text and the reference question text are related texts; reference actions corresponding to the at least one reference question text are determined, the reference actions are aggregated to obtain a reference action sequence, and the reference actions are used for indicating the satisfaction degree of the user on reference reply texts corresponding to the reference questions; an indication text is obtained according to the reference action sequence, the indication text, the reference text and the target question are input into a language model to obtain a target reply text, and the indication text is used for indicating the characteristics of the reference reply text with the satisfaction degree reaching a preset threshold value. By adopting the scheme, the technical problem of relatively low reply text generation efficiency in related technologies is solved.

The invention relates to a multi-language intelligent question and answer method and device, equipment and a medium in the technical field of e-commerce, and the method comprises the steps: obtaining a question text provided by a user, and determining a question language of the question text; when the question language is consistent with a knowledge language corresponding to the question and answer knowledge base, determining a pre-stored question matched with the question text in the question and answer knowledge base, and taking an answer text corresponding to the pre-stored question as a target answer text; when the question language is inconsistent with the knowledge language, translating the question text into a target question text corresponding to the knowledge language, determining a pre-stored question matched with the target question text in a question and answer knowledge base, and translating an answer text corresponding to the pre-stored question into a target answer text corresponding to the knowledge language; and sending the target answer text to the user. Only one question and answer knowledge base of a specific language needs to be maintained, and the management and maintenance workload of the question and answer knowledge base is remarkably reduced.

The invention provides a text generation method and device based on a large language model, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a target question text input by a user; retrieving at least one text paragraph related to the target question text from a pre-constructed data set; generating a first answer text based on a large language model according to the planning prompt template, the target question text and the related text paragraph; according to the initial planning lexical element and the first answer text, generating planning indication information; if the planning indication information is a first indication identifier, retrieving a supplementary text paragraph from the data set, and combining the answer prompt template, the first answer text and the updated planning lexical element to obtain a second answer text; and based on the second answer text, updating the planning indication information until the planning indication information is a second indication identifier, and outputting the generated answer text. According to the invention, the correlation and the fact correctness of large language model output can be improved.

The invention provides an intelligent question and answer processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a target question text in response to a received question and answer request, and performing intention analysis on the target question text to obtain an intention analysis result; when it is determined that the target problem text meets a problem dismantling condition based on the intention analysis result, dismantling the target problem text based on the intention analysis result and the target problem text to obtain a plurality of sub-problems; for each sub-problem, determining a first execution plan corresponding to the sub-intention corresponding to the sub-problem, and determining a first execution result corresponding to the sub-problem based on the first execution plan; and based on the first execution result corresponding to each sub-question, performing finite state transition, and determining a reply text corresponding to the target question text. According to the invention, the accuracy and controllability of intelligent question and answer processing can be improved.

The invention provides a construction method of an intelligent personalized interaction system. The construction method comprises the following steps: constructing a database; preparing a training set and a test set with problem texts, constructing a vocabulary of high-frequency words for the training set and the test set, and performing text coding; constructing a workflow scheduler for counting high-frequency problem types; the user portrait building module is used for obtaining a user portrait according to historical dialogues, user questions and high-frequency question types of the database; the intelligent personalized interaction system comprises a user question input interface, a question classification module and a database which are connected in sequence, and the database is connected with a workflow scheduler and a user portrait construction module. And outputting the user portrait and the high-frequency question type to a pre-trained large language model to generate a recommendation question. The invention further provides a corresponding system. According to the method, deep understanding, accurate analysis and personalized interaction of user questions are realized, so that the user experience is remarkably improved, and diversified requirements of users in different scenes are met.

The invention provides a method, a device and equipment for generating reply information based on a large language model, and relates to the technical field of artificial intelligence, in particular to the fields of document retrieval, natural language processing and large language models. According to the implementation scheme, in response to a received question text of a user, a semantic vector of the question text and event information related to a specific field are obtained; based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category, obtaining a plurality of candidate documents in a document library of a specific field; for a candidate document in the plurality of candidate documents, determining quality evaluation information of the candidate document based on the event category; and determining at least one target document in the plurality of candidate documents based on the relevancy between the candidate documents and the question text and the quality evaluation information of the candidate documents, so as to obtain reply information for replying the question text based on the at least one target document.

The invention provides a question answering method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a question text; performing semantic understanding on the question text through a large language model to obtain a structured data query instruction; retrieving in a database based on the data query instruction to obtain target data related to the question text, the data included in the database being obtained by integrating first business data obtained from at least one business system; and obtaining a target answer of the question text through the large language model according to the question text and the target data related to the question text.

The invention relates to the technical field of large models, and provides a thinking chain-based large model question and answer method and device. The method comprises the following steps: obtaining a subjective question text; inputting the subjective question text into a question and answer model to obtain a target answer output by the question and answer model; the target answer is an answer conforming to a social moral specification; the question and answer model is obtained by using a historical subjective question text and a historical target answer tag corresponding to the historical subjective question text to train the large model on the basis of expanding the reasoning process of the large model into a structured reasoning chain and applying budget constraint to the structured reasoning chain. According to the question and answer model, when subjective questions are processed, answers conforming to social moral specifications can be stably output, and the reasoning efficiency and accuracy are effectively improved in the reasoning process.

The embodiment of the invention relates to a reply text generation method and device, computer equipment and a storage medium, and the method comprises the steps: extracting a question entity and a question intention from a question text when the question text is obtained; determining a retrieval strategy and a target database corresponding to the question text according to the question intention; generating a prompt word corresponding to the question text through a reinforcement learning model, wherein the prompt word is generated based on feedback data of a user; retrieving a retrieval result corresponding to the question text from the target database according to the retrieval strategy and the cue word; and generating a reply text corresponding to the question text according to the retrieval result. Therefore, a fixed prompt word template does not need to be used, the prompt word corresponding to the current question text is generated through the reinforcement learning model, the generated prompt word is more targeted, then the reply text is generated based on the prompt word, and the accuracy of generating the reply text is improved.

The invention relates to a visual dialogue method and device based on external knowledge enhancement and semantic consistency, and the method comprises the steps: constructing a multi-modal scene graph through extracting the incidence relation between intra-domain visual objects and the incidence relation between intra-domain text vocabularies according to an obtained image, a historical dialogue text and a current question text, constructing a common sense knowledge base by extracting common sense knowledge triples; based on the common knowledge base, expanding the multi-modal scene graph and carrying out relation reinforcement learning to obtain characteristics of associated reinforcement nodes; obtaining instance-level representation by using a pre-trained large language model, obtaining text common sense-level representation based on the characteristics of the association enhancement node, and performing characteristic fusion for the instance-level representation and the common sense-level representation to obtain global fusion characteristics; and based on the fusion feature, obtaining an answer of the visual dialogue through reasoning. According to the method, semantic association can be carefully mined, so that the accuracy, richness and comprehensiveness of multiple rounds of visual dialogues are remarkably improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a personalized content recommendation method and system based on a large model and electronic device.The method comprises the steps that in response to a received content query instruction, at least one large language model is used for generating a question text corresponding to the content query instruction; determining a corresponding text block according to the similarity between each question text and the text block in the content library; based on a first prompt word generated according to each question text and the corresponding text block, using at least one large language model to generate a recommendation result corresponding to the question text; and outputting a recommendation result. On the basis of the scheme, the to-be-recommended content can be subjected to text division, the multiple problem texts are generated according to information such as user requirements, progressive retrieval is performed on the text library through the multiple problem texts, then the recommendation result meeting the personalized requirements of the user is generated on the basis of the retrieval result, the adaptability of the recommended content to the user is improved, and the user experience is improved. And the personalized requirements of the user are met.

The invention provides an answer text generation method and device, equipment, a storage medium, a program product and a government affair question-answering system, and can be applied to the technical field of artificial intelligence and the technical field of Internet. The method comprises the following steps: in response to an interactive operation of a user, inputting a question text related to the interactive operation into a large language model to obtain a reference answer text; in response to the condition that the reference answer text has a retrieval identifier, retrieval is performed in a basic text library based on the question text to obtain an intermediate answer text, and the retrieval identifier represents that the basic text library has an answer text related to the question text; and processing the intermediate answer text and the question text by using a large language model to obtain a target answer text.

The invention provides a toxic thinking chain analysis and optimization method and device for a large language model. The method comprises the steps that a question text is obtained and input into the large language model for text question answering; reasoning drift information loss existing when the toxic thinking chain is generated is analyzed through attribution tracking, or answer offset information loss existing when an answer text is generated according to the toxic thinking chain is analyzed through causal tracking; performing residual decoding on the toxic thinking chain to obtain an optimized thinking chain when reasoning drift information loss is analyzed, and performing text sequence position exchange on the question text and the toxic thinking chain in a thinking chain text fragment to obtain an optimized text sequence when answer drift information loss is analyzed; and generating an answer text by the large language model based on the optimized thinking chain or the optimized text sequence. Through the method and the device, the defect of low reasoning performance caused by the fact that an optimization method of a large language model toxic thinking chain cannot be universally adapted in a question and answer scene is overcome.

The invention relates to a question and answer method and device, computer equipment and a readable storage medium. The method comprises the following steps: inputting an initial question text of a user into a quantized language model to obtain word slot parameters corresponding to the initial question text; the word slot parameter comprises description information related to the target question and answer scene; the quantized language model is a language model obtained by compressing model parameters of the large language model; inputting the word slot parameters and the initial question text into the quantized language model to obtain a standardized question text; the standardized question text is a text which conforms to a preset text structure and clearly reflects the intention of the user; the standardized problem text is input into a vector model, and a plurality of documents with the highest correlation degree with the standardized problem text are obtained; and inputting the standardized question text and the plurality of documents into the quantized language model to obtain an answer text generated by answering the initial question text. The method can improve the reasoning speed of the question answering system.

The invention provides a Token compression method and device for large model dialogues, which can effectively reduce the number of Tokens (lexical elements) of texts input into a large model in multiple rounds of dialogues, and can also retain key information of early parts of historical dialogue texts at the same time. The method is applied to a Token compression management agent, and comprises the following steps: after obtaining a question text input by a user to a terminal, determining the number of lexical elements in an initial text (including a historical dialogue text and the question text) to be sent to a large model; if the initial text lexical element number does not meet the threshold requirement, switching the state into a lexical element compression state, interacting with the large model to obtain the lexical element number of compressed intermediate dialogue texts (parts except for the previous k rounds of dialogue texts, k is greater than or equal to 0), and keeping a semantically generated abstract text. And splicing the first k rounds of dialogue texts, abstract texts and question texts to obtain a target text. If the number of the target text lexical elements meets the threshold requirement, sending to the first large model.

The embodiment of the invention discloses a cue word generation method, a cue word-based dialogue method and a related device. The cue word generation method comprises the following steps: acquiring a plurality of sample question and answer pairs about a target field; according to the initial prompt word and the plurality of question sample texts, obtaining answer training texts corresponding to the question sample texts through a natural language dialogue model; and according to the plurality of answer training texts and the plurality of answer sample texts, adjusting the initial cue word until a preset condition is met to obtain a target cue word. The dialogue method based on the cue word comprises the steps of obtaining a question text and a target cue word, wherein the target cue word is obtained through the method; according to the question text and the target prompt word, an answer text corresponding to the question text is obtained through a natural language dialogue model, and the answer text is related to a target field corresponding to the target prompt word. By adopting the embodiment of the invention, the target cue word can be efficiently generated, and the accuracy of the target cue word indicating that the natural language dialogue model generates the answer text is relatively high.