73 results about "Reference Document" patented technology

Embodiments of the present description provide a document retrieval method and an automatic question answering method. The document retrieval method comprises: obtaining data for retrieval; retrieving at least one candidate document from among a plurality of documents in a knowledge base on the basis of the data for retrieval; selecting at least one reference document from among the at least one candidate document on the basis of the association relationship between the data for retrieval and the at least one candidate document; and on the basis of the at least one reference document, updating the data for retrieval, to obtain updated data for retrieval, and retrieving a target document from among the plurality of documents by means of the updated data for retrieval. A reference document is obtained by means of coarse ranking retrieval and fine ranking retrieval, and thus, the accuracy of the reference document is guaranteed; the reference document is used for updating data for retrieval, so that positive and negative feedback interactions are achieved in the retrieval pipeline, making the data for retrieval more accurate, effectively solving retrieval errors caused by expression diversity and indirectness, and improving the accuracy of document retrieval.

The embodiment of the invention discloses a personalized writing auxiliary method and device, equipment and a storage medium. The method comprises the steps of obtaining question information, fact data, text style input information and a reference document of a to-be-generated document; utilizing a pre-trained language processing model to generate outline information of the personalized style according to the question information, the text style input information and the fact data; according to the outline information, the fact data, the text style input information and the reference document, writing document content is generated in an auxiliary mode. On the basis, the three dimensions of fact data, text style input information and the reference document are added to serve as writing auxiliary data support, writing efficiency, quality and integrity are improved, meanwhile, the text style of an author can be comprehensively considered, and the content of the writing document generated in an auxiliary mode better fits the writing style of the author.

Evaluating and improving information retrieval in question-answering systems is an area of importance in machine learning growth. Retrieval components in a retrieval-augmented generation (RAG) question answering system enable machine learning models to provide more accurate and reliable answers to questions. Systems for retriever evaluation involve processing queries in comparison to reference documents. The system first retrieves documents deemed relevant, then generates a first answer based on them. A second answer is generated using a set of documents that includes ground truth documents known to be relevant to the query. By analyzing semantic overlap between these responses, a quantitative evaluation of the retrieval component is obtained. This evaluation then informs automatic modifications to retrieval parameters, enhancing future document selection and response accuracy.

A method for sorting candidate documents into several sets associated with a reference document, each document stored by a client device memory, wherein the method includes a device processor performing for each reference document, generating a first prompt for a first large language model requesting generation of at least one question determining the relevance of a candidate document to a reference document; (b) for each candidate document, generating at least one second prompt for a second large language model requesting the answer to at least one reference question; assigning each candidate document to the set associated with a reference document as a function of the value(s) that has been received for a second prompt containing a reference question associated to the reference document.

The invention provides a document generation method and device, and relates to the technical fields of document processing, artificial intelligence, natural language processing, computer vision and the like. According to the specific implementation scheme, a reference document set and document description information are determined based on document demand information of a user; performing visual feature recognition on the reference document set by adopting a multi-modal large model to obtain a visual feature set; performing content semantic and content relationship identification on the document description information by adopting a first large model to obtain data structure information; determining a target reference document from the reference document set based on the visual feature set and the data structure information, and generating document planning information; and based on the document planning information, adjusting the target reference document to obtain a target demand document.

The invention discloses a document revision method, a document revision device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, acquiring a reference document and a to-be-revised document in a revision mode; and analyzing the reference document, and extracting structured revision information corresponding to the reference revision operation trace. And analyzing the to-be-revised document to generate structured document information containing target content and target format information. And then, generating a cue word based on the semantic intention of the structured revision information, and inputting the cue word and the structured document information into the target model to obtain target revision information. And finally, mapping the target revision information to the to-be-revised document, and generating a target document with a target revision operation trace. According to the document revision method, the normalization and accuracy of the revision result can be ensured, the manual intervention cost is reduced, and format errors and semantic errors are reduced.

The invention relates to the technical field of document management, and relates to a legal document key point automatic extraction and association method and system.The new legal document data of a target case is obtained, a preset document fingerprint algorithm is used for automatically comparing the new legal document data with a historical reference document, differential text data fragments are extracted, and the text data fragments are extracted; the method comprises the following steps of: performing deep semantic analysis on different text data fragments to generate candidate triple data containing entities and relationships, intelligently mapping the candidate triple data into a pre-constructed case knowledge graph, quickly positioning graph anchor nodes with data change, and then, performing data change on the basis of change types of the different text data fragments. And executing atomic updating operation on the map anchor node, including updating the dynamic mark of the node state attribute, and finally generating the change source node data with the latest state identifier. The problems that the current legal case data updating depends on manpower, is low in efficiency and is easy to make mistakes are effectively solved.

Evaluating and improving information retrieval in question-answering systems is an area of importance in machine learning growth. Retrieval components in a retrieval-augmented generation (RAG) question answering system enable machine learning models to provide more accurate and reliable answers to questions. Systems for retriever evaluation involve processing queries in comparison to reference documents. The system first retrieves documents deemed relevant, then generates a first answer based on them. A second answer is generated using a set of documents that includes ground truth documents known to be relevant to the query. By analyzing semantic overlap between these responses, a quantitative evaluation of the retrieval component is obtained. This evaluation then informs automatic modifications to retrieval parameters, enhancing future document selection and response accuracy.

The invention provides a document consistency auditing method and system based on a large model and a storage medium, and the method comprises the steps: obtaining a to-be-audited document, inputting the to-be-audited document into a pre-trained large model, and carrying out the fragment division to obtain segmented documents; information extraction is carried out on the segmented documents to obtain document extraction information, information filtering is carried out on the document extraction information to obtain extraction filtering information, and the document extraction information comprises attribute names, attribute values, extraction original texts and reference documents; and carrying out attribute alignment on the extraction filtering information and attribute standard information in the reference literature, and generating a document consistency auditing result according to an attribute alignment result. According to the embodiment of the invention, the extraction filtering information and the attribute standard information in the reference document are subjected to attribute alignment, so that the to-be-extracted document and the reference document can be effectively subjected to semantic consistency auditing, and the accuracy of document consistency auditing is improved.

The invention provides a document comparison auditing method and a related device, and relates to the field of artificial intelligence, the method comprises the following steps: splitting a first document into a plurality of document segments to obtain a first document segment set; the first document is a to-be-audited document; obtaining a second document fragment set with the highest similarity with the first document fragment set; the second document fragment set is a document fragment set determined from the reference document fragment set; utilizing a first model to extract first difference content of the sum of the first document fragment set and the second document fragment set; based on the first difference content, utilizing a second model to perform further auditing on the first document fragment set, and determining second difference content of the first document fragment set relative to the second document fragment set; the complexity of the second model is higher than that of the first model; and generating an audit report based on the second difference content. According to the method, the document auditing efficiency is effectively improved.

Embodiments of the present disclosure are directed to generating content filer forms from a set of documents uploaded to a cloud-based collaboration environment. Generating content filer forms from a set of documents can comprise uploading the set of documents, pre-processing the uploaded documents to determine one or more document types in the set of documents and an intent for each determined document type, generating a natural language prompt for each document type based on the intent for each document type and one or more reference documents defining constraints on the natural language prompt, generating, from each natural language prompt a content filer form associated with each document type using a generative Artificial Intelligence (AI), and refining each generated content filer form.

The invention discloses a data exception identification method and system based on machine learning, and the method comprises the steps: obtaining reference document data, carrying out the document analysis and document enhancement, obtaining standard document data, carrying out the multi-modal feature layering extraction according to the standard document data, and obtaining multi-modal document features, performing feature edge processing according to the multi-modal document features to obtain combined document features, obtaining document density and document statistical data according to the multi-modal document features, calculating document anomaly coefficients, and constructing a self-adaptive data anomaly recognition model, and inputting to-be-identified document data into the adaptive data exception identification model to obtain a document data exception identification result. The method not only can improve the accuracy and comprehensiveness of document data exception recognition, but also has better interpretability, and can be directly applied to a data exception recognition system.

The invention provides an evaluation set generation method, equipment and a product. The evaluation set generation method comprises the steps that an RAG knowledge base containing knowledge documents and a question set of evaluation questions are acquired; extracting a theme of the knowledge document, and determining a document identifier of the knowledge document; constructing a first evaluation data set by using the theme and the document identifier; adding the question types into the first evaluation data set by utilizing a matching relationship between themes in the first evaluation data set and the question types in the question set to obtain a second evaluation data set; and inputting the second evaluation data set into the large model, and generating an evaluation set containing the document identifier, the theme, the question type, the question, the answer and the reference document.

The embodiment of the invention discloses a dialogue processing method and device and a computer program product, and relates to the technical field of data processing and artificial intelligence. In dialogue processing based on the retrieval enhancement generation technology, a traditional dialogue text and document block matching mode is replaced with the dialogue text and document block entity matching mode, and the target document block entity obtained through matching in the mode better reflects key information of the dialogue text; therefore, by taking the document block associated with the target document block entity as the reference document block, the reference document block can contain the key information of the dialogue text, and the problems of'inaccurate checking 'and'incomplete checking' caused by a traditional dialogue text and document block matching mode due to the fact that the document block contains noise are avoided; the accuracy and comprehensiveness of the reference document block are improved, and then the accuracy of the reply obtained based on the reference document block is improved.

The embodiment of the invention discloses a document checking method and device, electronic equipment and a storage medium. The method comprises the steps of calculating a similar hash value of a to-be-checked document based on a reference vocabulary to obtain a hash value of the to-be-checked document; based on the to-be-checked document hash value and candidate document hash values corresponding to the candidate reference documents, reference documents similar to the to-be-checked document in the candidate reference documents are determined, and the candidate document hash values corresponding to the candidate reference documents are similar hash values, calculated based on a reference vocabulary, of the corresponding candidate reference documents; respectively extracting feature vectors of each sentence in the reference document and the to-be-checked document to correspondingly obtain a plurality of reference sentence vectors and a plurality of to-be-checked sentence vectors; and based on the similarity between each to-be-checked sentence vector and each reference sentence vector, determining a reference text corresponding to each sentence in the to-be-checked document in the reference document. According to the embodiment of the invention, the efficiency and accuracy of rechecking and checking the to-be-checked document can be improved.

The present invention belongs to the technical field of project management systems, and provides a method, device and electronic device for recommending reference documents for a project management system. The recommended method includes the following steps: S1: Extract the project task content, and obtain a number of keywords by processing the project task content based on natural language processing tools; S2: Search for files in a pre-established knowledge base according to the number of keywords; S3: Sort the searched files, and respectively establish corresponding file links in the project tasks. The recommended method analyzes the task content based on natural language processing technology, automatically extracts the key task content, assists users in understanding the key points of the tasks, and actively searches for and provides corresponding file resources, improving resource utilization and enhancing the efficiency of users to complete tasks.

The invention provides a model training method, a question answering method and a related device, and relates to the technical field of machine learning. The method comprises the steps of firstly obtaining a first sample data set and a reference document set; then a prompt text is constructed based on the first sample data set and the reference document set, the prompt text is used for prompting a machine learning model to select a reference document used for replying the first question data from the reference document set, and first answer data used for replying the first question data is generated; and finally, training a machine learning model according to the prompt text to obtain a question and answer model. Therefore, the machine learning model is trained in a thinking chain enhancement mode according to the prompt text, how to select related reference documents from the reference document set according to given questions can be learned, and key information is extracted to generate accurate answers; therefore, the training efficiency of the model is improved, the accuracy of the question and answer task is improved, and the trained model can adapt to large-scale data processing requirements.

This invention relates to the fields of artificial intelligence and software testing technology, and provides a content compliance review method for a "testing brain" based on KG and RAG collaborative knowledge enhancement. The method includes: acquiring a set of reference documents and obtaining the knowledge graph and high-dimensional vector corresponding to each reference document to construct a knowledge base; receiving a report to be reviewed; on the one hand, comparing the similarity between the retrieval vector of the report to be reviewed and the high-dimensional vector of the knowledge base to retrieve highly relevant reference documents from the knowledge base; on the other hand, selecting highly relevant subgraphs from the knowledge graph based on the entities in the report to be reviewed; fusing the highly relevant reference documents and highly relevant subgraphs to form structured knowledge, and generating structured prompts based on the structured knowledge; and outputting the compliance review results of the report to be reviewed based on the structured prompts and structured knowledge. This invention can significantly improve the efficiency, accuracy, and interpretability of software testing compliance review.

An information processing apparatus comprises: an acquisitor to obtain document information, including character strings and position data from document image data; a converter to transform the acquired document information into a distributed representation; an information extractor to identify a character string corresponding to an item specified by a prompt, using a large language model by inputting the prompt with the acquired document information; a storage unit to save the document information, distributed representation, and extraction results, associating them with the document; and a selector to choose a reference document from previously processed documents based on distributed representations. The prompt for processing a new document includes details about the selected reference document.

The invention provides a document analysis result evaluation method and device and electronic equipment, and the method comprises the steps: obtaining a reference document and an analysis document corresponding to a target document, each of the reference document and the analysis document comprising a plurality of document tags and at least one standardized tag under a standardized framework; according to the document label and the standardized label in the reference document, a first data report is generated, the document label and the standardized label in the document are analyzed, a second data report is generated, each of the first data report and the second data report comprises a plurality of report items, and each report item is used for describing document content from one dimension; and determining an evaluation result of the parsed document according to the first data report and the second data report. The analysis document can be evaluated from multiple dimensions, so that overall and integral evaluation results are obtained.

The invention discloses an automatic test script generation method and device, a storage medium and electronic equipment. Comprising the steps that a current project file is recognized, under the condition that the project file is changed, a target document structure and a target style structure are generated based on the current project file, the target document structure is used for indicating the content of the current project file, and the target style structure is used for indicating the style of the current project file; comparing the target document structure with the reference document structure, and comparing the target style structure with the reference style structure; under the condition that changed front-end elements exist between the target document structure and the reference document structure and / or between the target style structure and the reference style structure, the test script corresponding to the project file is updated based on the changed front-end elements, and the front-end elements are used for indicating interactive or visual elements in the project file. The technical problem that an automatic test script generation method provided by the related technology is low in updating efficiency is solved.

The invention discloses a question and answer retrieval method and device, and the method comprises the steps: obtaining a target question, and determining a plurality of sub-questions included in the target question; determining a first group of document slices matched with keywords of the target question and the plurality of sub-questions and a second group of document slices matched with semantics in a preset document slice library; and taking an intersection of the first group of document slices and the second group of document slices to obtain a reference document, and inputting the target question and the reference document into a preset language model to obtain a target answer for the target question.

The invention relates to a document processing method and device, equipment and a storage medium. The method comprises the steps of obtaining a reference document, and performing paragraph segmentation on the reference document according to a document structure of the reference document to obtain first metadata of each paragraph in the reference document; positioning a paragraph to be verified in the reference document according to the first metadata and the existence condition of a historical version document of the reference document; and according to the target paragraph content of the paragraph to be checked, searching a to-be-processed document from the historical document, and according to the target paragraph content, revising the to-be-processed document. By adopting the method, the document processing efficiency and the accuracy of a document result can be improved.

The invention discloses a long document conflict comparison method based on a multi-modal large model, and belongs to the technical field of document processing. The method aims at solving the technical problems that existing document conflict comparison can only recognize literal conflicts in a shallow layer, an NLP small model is poor in long text processing effect, and when a large model processes a long document, the computing power cost is high, semantics are prone to being lost, and compression is not thorough. According to the technical scheme, the method is characterized by comprising the steps of receiving, analyzing and converting a to-be-compared document and a reference document into a standardized format, splicing a text to a cue word and performing Token processing, executing three-level progressive dynamic compression if the text exceeds the limit, inputting the processed cue word into a multi-modal large model, completing literal and semantic layer conflict comparison by the model, and finally obtaining a comparison result. If the compression is invalid, executing a batch and document splitting bottom solving strategy, and finally formatting and outputting a comparison result.

A computer system may obtain a fact extraction prompt. The fact extraction prompt is configured to control how a fact generating machine learning model extracts or summarizes content of reference documents included in a corpus of documents. The computer system may input, into the fact generating machine learning model, the fact extraction prompt and one or more reference documents from the corpus of documents to identify one or more facts included in the one or more reference documents, populate respective data fields of one or more fact objects based upon the one or more facts identified by the fact generating machine learning model and generate one or more fact summaries for the matter based on the one or more fact objects. The one or more fact summaries include structured presentations of at least some of the one or more fact objects according to contents of the data fields.

The invention provides an Agent autonomous planning multi-mode large model content creation method, device and system, and relates to the technical field of large model content creation, and the method comprises the steps: generating a venation structure skeleton of a to-be-created content according to a writing requirement and a reference file; performing related information data collection according to the plurality of to-be-retrieved questions corresponding to each piece of paragraph information, and generating paragraph content corresponding to each piece of paragraph information; generating a chart or a picture corresponding to each piece of anchor point information according to each piece of anchor point information; obtaining each sentence needing reference calculation in each paragraph content to obtain a reference sentence list A; traversing the similarity list eta, and if eta i is greater than eta ', determining that Ai is not abnormal; generating a multi-modal long text corresponding to the writing requirement input by the user; proofreading the written content; according to the method, the intelligence, the reliability and the diversity of AI writing are remarkably improved by innovatively combining dynamic knowledge acquisition, multi-modal generation, long writing planning and content credibility guarantee.

The invention discloses a question answering method and device, a nonvolatile storage medium and electronic equipment, and relates to the field of electric digital data processing. The method comprises the steps that a plurality of candidate document slices corresponding to a question text and identification information of the candidate document slices are determined, each candidate document slice comprises a natural segment in a knowledge document, and the identification information comprises positioning information of the candidate document slices in the knowledge document; generating a first reply text according to the candidate document slices; determining a target character recorded in the candidate document slice in the first reply text, and performing highlight rendering processing on the target character to obtain a second reply text; and displaying the second reply text and the identification information of the candidate document slices. The technical problem that the credibility of the large model reply result cannot be guaranteed due to the fact that whether the large model is the reply text generated according to the reference cannot be determined in related technologies is solved.

This invention relates to the field of data processing technology and discloses a data query method, apparatus, computer equipment, and storage medium. The method includes: receiving text content input to a platform front-end page to obtain target text; when the target text includes filtering parameters, passing the filtering parameters to a target mapping file through annotations; constructing a fuzzy query statement in the target mapping file and performing a fuzzy query to obtain a first list of standard document information; during a repeal operation, receiving the standard number of the repealed standard document, repeating the above operation to obtain the first list of standard document information, and then changing its status to a questionable state. The repeal operation is used to uniformly change the status of standard documents referencing the repealed state to a questionable state. This invention achieves rapid retrieval and query of terminology and normative reference documents, as well as the function of associating standard status with normative reference documents, improving retrieval efficiency and accuracy.

Systems and methods for assessing similarity of documents are provided. Embodiments of the systems and methods include extracting a reference document text from a reference document, extracting an archived document text from an archived document, and quantifying the reference document and the archived document. The systems and methods may also include determining a document similarity value of the quantified reference document and the archived document. Determining the document similarity value includes calculating a set of vector similarity values for a set of combinations of a reference document text vector and an archived document text vector, and calculating the document similarity value, including a sum of the plurality of vector similarity values.