1078 results about "IntraText" patented technology

A method of generating outputs in LLMs including receiving including textual content, defining a context for the documents including identifying a topic or a category, segmenting the textual content into content chunks associated with the topic or category, assigning a tag to each content chunk, identifying selected chunks, adding metadata to the selected chunks indexing the selected chunks, receiving a query, and performing a response generation process including determining if a cache includes information for the query and either retrieving the information from the cache or performing a search of the index to retrieve the information, generating an augmented query by augmenting the query with retrieved information, generating a response from the augmented query, evaluating the response for compliance with criteria, and one of generating a final response and transmitting the final response to the user or performing a fine-tuning process comprising redefining the of the one or more contexts.

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

The invention discloses a hybrid enhanced index and system based on vector retrieval and a BM2 algorithm, and the method comprises the steps: uploading a document by a user, analyzing the document by the system to obtain text content, segmenting the text content, converting the segmented text content into dense vectors, storing the dense vectors in a vector library, extracting keywords of segmented texts by using a large model, processing the keywords, and inserting the keywords into a word list. Word segmentation is carried out on the text based on the constructed word list and a word segmentation device; in response to the received user query, vectorizing the user query, and calculating the similarity with each vector in the vector library to obtain a preliminary query result; performing word segmentation on user query, performing keyword retrieval by using a BM25 algorithm according to a word list and a word segmentation device to obtain query results, and filtering the similarity of the two query results by the reordering model according to a set threshold value; selecting a result with the highest similarity according to large model parameter limitation and an upper limit set by business requirements; and splicing the document content corresponding to the result and the user query into a cue word, inputting the cue word into the large model, and analyzing a semantic relationship and a logic structure in the cue word to generate an answer.

The invention provides a knowledge graph open domain construction and RAG question and answer method and device and a storage medium. The method comprises the steps of obtaining text content in a text file; performing text division on the text content to obtain text blocks; performing open domain information extraction on each text block by utilizing a large language model to obtain an initial triple; performing post-processing on the initial triad to obtain a post-processing triad for constructing the knowledge graph; constructing a knowledge graph according to the post-processing triple; the method for realizing RAG question answering based on the knowledge graph comprises the following steps: performing knowledge graph retrieval on a question to obtain a context related to the question, then constructing a question answering cue word by using the context and the question, and sorting out an answer to the question by a large language model based on the question answering cue word. According to the method, the problem that a complete knowledge graph cannot be obtained during defined domain knowledge graph construction in the face of diversified entities and relation types is solved, and the problems that RAG retrieval information is weak in correlation with problems and the multi-hop reasoning capability is limited are solved.

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 belongs to the technical field of natural language processing and information retrieval. The invention provides a bidding document multi-modal duplicate checking method and system based on a large model, and the method comprises the steps: carrying out the structural analysis and multi-modal feature extraction of an input bidding document, and generating the feature representation of semantic blocks and non-text elements; performing deep semantic coding on text blocks by using a dynamic context-aware large language model, and retaining logic relevance of a long text in combination with hierarchical position coding; efficient matching of massive semantic vectors is achieved through a mixed retrieval framework, and calculation efficiency is optimized in combination with a distributed calculation framework and a hardware acceleration instruction; and finally, performing structured feature reconstruction on non-text contents such as tables, charts and the like to realize cross-modal semantic association analysis.

A system and a method for augmenting a dataset comprising a textual content using instruction to cause a conversational language model to create variations of text items by changing text properties such as length, style, terminology, dialect, rhyming and the like. The method may also be used with combined prompts and iteratively.

Embodiments support systems and methods for large language model (LLM)-assisted research tools that support retrieval-augmented content generation. In an aspect, the disclosed systems and methods may provide functionality for receiving, by one or more processors, a set of search criteria via a graphical user interface. The systems and methods may also include functionality for providing, by the one or more processors, the set of search criteria or information derived from the set of search criteria as one or more prompts to one or more LLMs, and for generating, by the one or more LLMs, textual content based on the one or more prompts. The textual content may include information associated with one or more legal issues associated with the set of search criteria.

A method and an electronic device for generating article content are disclosed. The method includes: constructing a subject keyword library by collecting industry trend data, and collecting content materials for article generation; in response to a request for generating an industry information article, determining a target subject keyword and a prompt word text for dialogue with an artificial intelligence AI large language model, to facilitate a generation of an article subject; screening multiple segments of target content materials from the content materials; constructing a prompt word text for dialogue with the AI large language model according to the article subject and the target content materials to generate an article outline, the article outline including multiple sub-subjects; and calling the AI large language model multiple times to generate corresponding text contents for the multiple sub-subjects respectively.

Implementations relate to generating multi-modal response(s) through utilization of large language model(s) (LLM(s)) and other generative model(s). Processor(s) of a system can: receive natural language (NL) based input, generate a multi-modal response that is responsive to the NL based output, and cause the multi-modal response to be rendered. In some implementations, and in generating the multi-modal response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, textual content and generative multimedia content for inclusion in the multi-modal response. In some implementations, the generative multimedia content can be generated by another generative model (e.g., an image generator, a video generator, an audio generator, etc.) based on generative multimedia content prompt(s) included in the LLM output and that is indicative of the generative multimedia content. In various implementations, the generative multimedia content can be interleaved between segments of the textual content.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

A data processing system implements and iterative process for dynamically creating a template including receiving a request for a template suggestion for an electronic content item for an application on a client device. The request includes an identifier of the application and textual content from the first electronic content item. The system further implements providing the identifier of the first application and the textual content to a language model to obtain a template suggestion, the language model being trained to analyze the identifier of the application and the textual content and to output the template suggestion, the template suggestion comprising a template identifier and a first subsection identifier identifying a first subsection of the first template predicted to be relevant to the user for creating the electronic content item, sending the template suggestion to the client device, and causing the client device to present the template suggestion in the application.

The invention relates to a document processing method and system based on text content extraction. The method comprises the steps that an original document containing text, image and format information is received, the encoding format of the document is automatically detected, character set conversion is executed, and hierarchical indexes including page numbers, paragraphs and tables are established for an unstructured document; the method comprises the following steps: synchronously processing text content and visual layout through a pre-trained visual-language model, extracting word-level and sentence-level semantic features by a text stream embedding layer, analyzing spatial distribution features of document elements by a visual encoder, and fusing text and visual features through a cross-modal attention mechanism; and loading the domain knowledge graph matched with the document type, and executing entity linking to associate the text mentions to the knowledge nodes. According to the document processing method and system based on text content extraction, through the synergistic effect of vision-text joint coding and knowledge enhancement, the accuracy of financial contract key clause recognition tasks is improved, the error rate is lower than that of industry benchmark products, and the semantic understanding precision is remarkably improved.

The invention discloses a multi-literature intelligent classification and automatic review generation method based on a large language model, which is characterized by comprising the following steps of: retrieving literatures or uploading local literatures, and extracting text contents; identifying keywords by utilizing an algorithm or a large language model; converting the keyword into a vector form by using a vectorization model; summarizing the research direction of the clustering result by using a large language model; comprehensively summarizing the single literature and literatures in different research directions by using a large language model; and executing the steps of reviewing, quotation insertion and the like through a large language model and a vectorization semantic matching technology. Compared with the prior art, the method has the advantages that the efficiency and quality of document classification and review generation are remarkably improved, technologies such as text vectorization, topic clustering and large language model generation are well fused, efficient and accurate multi-document review automatic generation is achieved, the method is suitable for interdisciplinary academic document analysis and information integration application, and the method is suitable for popularization and application. Wide application potential is shown.

The invention provides a text information structured recovery method and system based on a large language model and application. The text information structured recovery method comprises the following steps: S1, extracting original text content from a webpage or an unstructured document; s2, designing a cue word template according to different scenes and target structures, and generating cue words; s3, guiding a large language model to analyze the original text content and the cue word in the step S2, and generating a text result with a hierarchical structure; s4, analyzing a text result in the step S3, constructing a semantic structure tree, and forming a multi-layer nested structure; and S5, applying the multi-layer nested structure in the step S4 to database modeling and content indexing, compared with the defects of non-uniform structure loss, poor universality and semantic understanding intelligence deficiency in the prior art, the manual processing cost can be remarkably reduced, the data structuring efficiency and accuracy are improved, and the semantic understanding intellectuality is improved. And a stable and high-quality structured text support is provided for a large model ecological system.

The invention discloses a large model text auditing optimization method based on clustering preprocessing, which relates to the technical field of natural language processing, and comprises the following steps: S1, preprocessing input original text data, including text cleaning, word segmentation, stop word and punctuation mark removal and standardized text vector generation, during preprocessing, a TF-IDF algorithm is adopted to calculate a text feature weight, and a high-dimensional feature vector matrix is generated. According to the large model text auditing optimization method provided by the invention, the text is divided into clusters with similar semantics or theme contents through clustering preprocessing, and a special auditing model is constructed for each cluster, so that the models can more accurately understand the text contents, the auditing accuracy is improved, and meanwhile, the auditing efficiency is improved. Due to the fact that the data size needing to be processed is reduced through clustering, the calculation efficiency of the model is remarkably improved, the clustering center vector and the cluster division rule are continuously adjusted, and closed-loop optimization of the model is achieved.

A system may access content comprising text content, visual content, and / or audio content. The system may perform, based on a harmonization model and / or consistency model, a harmonization check and / or a consistency check on the content. The system may recognize, based on the harmonization check and / or the consistency check, a conflict to be corrected. The system may identify a property of the content that should be changed based on the recognized conflict. The system may generate a corrective action based on the property of the content that should be changed.

The invention relates to an automatic video editing method based on semantic analysis, and belongs to the technical field of video editing. The method comprises the following steps: extracting anchor point information of text content in a streaming media file based on a knowledge graph, establishing a support relationship, and determining a main anchor point, a sub anchor point and a argument anchor point by combining the support relationship and the anchor point information; constructing a dynamic trigger mechanism, setting a verification direction of the dynamic trigger mechanism, determining a semantic extension direction of the anchor point information according to the verification direction, and verifying whether a precondition chain supporting the anchor point information exists or not; and setting a credibility scoring strategy, determining the credibility of the anchor point information according to the logic complexity, the language confidence and the text consistency, forming a sequence set of the anchor point information, and splicing to form a logically coherent video editing text result. According to the method, automatic and intelligent editing of the video is realized based on semantic analysis, and the editing efficiency and quality are remarkably improved.

Recent developments in machine learning (commonly coined "artificial intelligence" or "AI") have vastly expanded applications for this technology, such as myriad "chat" agents adept at understanding natural human language. While state of the art generative models can parse text queries from a user and provide comprehensive, accurate responses (including generating images depicting desired content), current implementations struggle with understanding all information present in images of documents, especially images of business documents. In particular, generative models fail to understand structured and semi-structured information, e.g., as indicated by graphical information such as lines, geometric relationships (e.g., indicated by tables, graphs, figures, etc.), formatting, and other contextual information that human readers easily and implicitly understand. The disclosed inventive concepts transform structured and semi-structured information along with textual content into a textual representation that allows generative models to better understand textual content and non-textual structured information present in document images.

A system and method are provided for dynamically modifying textual content by applying a text analysis tool and selectively using a large language model (LLM). The method includes applying a text analysis tool to text having been added to a document, identifying a portion of text in the document as violating a rule in a rule set associated with the document, and providing a first input to a large language model (LLM). The first input comprises at least one prompt requesting a revision to the portion of text in the document. The method also includes receiving, from the LLM, a response to the first input, the response comprising a suggested modification to the document related to the portion of text; and providing an option to apply the suggested modification to the document.

The invention discloses a reading sequence detection method and system combining layout analysis and a language model. The method comprises the following steps: collecting a historical handwritten document image and preprocessing the historical handwritten document image; performing layout analysis on the document image, and identifying and classifying interested text areas in the document image; performing semantic similarity evaluation on different types of layout entities in combination with a language model; and detecting a reading sequence by using a sorting algorithm through document layout and semantic information. The method has the advantages that the reading sequence detection method for the historical handwritten document image is provided through combined recognition of the text and the layout, and conversion of the document from the image to the text content conforming to the reading sequence is achieved.

The embodiment of the invention provides a document processing method and device and an information retrieval method and device. According to the document processing method, the text content of a target document can be subjected to fragmentation processing to obtain a plurality of text fragments, then each text fragment is input into a first large language model for model processing to obtain a processing result, the processing result comprises a plurality of knowledge fragments, and then the target document is processed according to the knowledge fragments. And generating first tag data corresponding to a plurality of preset tag categories, and correspondingly storing each knowledge fragment and the first tag data in a first database, so that the first tag data is used for knowledge question and answer retrieval. In the information retrieval method, after a user question in a text form is determined according to an information retrieval request of a user, second label data corresponding to the plurality of label categories are generated according to the user question, and then the user question is retrieved based on first correlation between at least part of first label data in a first database and the second label data. Knowledge fragments related to the user question are retrieved.

The invention relates to the technical field of text inspection and analysis, in particular to a localized bidding document error inspection method based on a large language model, which comprises the following steps of: constructing a bidding requirement knowledge graph which comprises a plurality of requirement item nodes, the requirement item node attribute comprises requirement content, a chapter to which the requirement item node attribute belongs and an importance level; establishing a preliminary mapping relationship between the requirement item node and the text content set, and generating a structured bidding file parse body; and carrying out double-round model analysis and error checking, generating an error record for the items judged to be not satisfied, and outputting a structured error list comprising the non-satisfied items and reasons of the non-satisfied items. The method improves the accuracy and coverage of error checking, and is especially suitable for checking scenes of bidding and tendering documents with numerous and jumbled contents and various formats.

The invention relates to the technical field of natural language processing, in particular to an AI reading management and control method and system based on artificial intelligence, and the method comprises the following steps: carrying out text word segmentation processing on an input original text, segmenting the text into independent sentences, recognizing basic word units in each sentence, analyzing semantic adjacency relationships and syntactic structure features among vocabularies, and carrying out word segmentation processing on the word units; screening and extracting potential phrases representing paragraph meanings, and establishing a candidate semantic unit set; according to the method, through word segmentation, syntactic structure recognition and semantic adjacency analysis of the original text, potential phrases capable of representing paragraph significance are extracted, the candidate semantic unit set is constructed, and modeling of the semantic structure in the text is achieved. And associating the set with the reading fixation duration and the playback action of the user sentence by sentence to obtain a reading behavior response of each semantic fragment, and executing semantic weighting and reading behavior cross analysis according to the reading behavior response. Through the linkage mode, the semantic focus actually focused by the user at present can be recognized.

The invention discloses a retrieval enhancement generation method and device based on a large model and a medium, and the method comprises the following steps: receiving query information of a user, and adopting an intention recognition module to recognize a knowledge base to be queried by the user according to the query information and label data of the knowledge base; the number of the knowledge bases is at least two, document types stored in each knowledge base are different, and the tag data comprises category tags representing categories to which the knowledge bases belong and entries representing key contents of documents in the knowledge bases; rewriting the query information based on semantic maintenance to obtain a standard query statement; retrieving a document with the highest similarity with the standard query statement from a knowledge base to be queried by the user based on the standard query statement; and combining the standard query statement and the document with the highest similarity with the standard query statement to form a context environment, and inputting the context environment into the trained language model for text content writing. By adopting the method, the accuracy of the document obtained by synthesizing the response can be improved.

The invention relates to the technical field of large language models, in particular to a question and answer method for a structured long document, which comprises the following steps of: S1, analyzing documents in different formats, and constructing structured metadata of the documents according to analysis results; s2, dividing the document into a plurality of text segments, vectorizing each text segment, and storing the vectorized text segments in a special vector database; s3, respectively constructing a plurality of text content acquisition tools for extracting text contents of different parts in the document; designing and implementing a vector-based retrieval tool, wherein the vector-based retrieval tool is used for searching text fragments related to user query in a vector database; s4, constructing an Agent containing a plurality of text content acquisition tools and retrieval tools, and intelligently selecting the text content acquisition tools or the retrieval tools according to the user questions to acquire related text contents required by the LLM to answer the questions; and S5, after the related text content is acquired, analyzing the related text content through LLM to generate a final answer.

The invention belongs to the field of natural language processing and artificial intelligence, and particularly relates to a long text writing generation method and system based on multi-agent cooperation. The method comprises the following steps: receiving a text theme input by a user, completing complex theme decoupling, and outputting an outline directory; the method comprises the following steps: collecting keywords according to a text topic and an outline catalog, obtaining a retrieval result by retrieving the Internet, a domain database and network resources, and preprocessing to obtain a reference with a uniform format; performing systematic evaluation on the reference literature from the field integrating degree and the logic relevance, and performing grading processing on the reference literature to obtain a high-quality reference literature; based on the text theme and the high-quality reference, long text content is generated according to a set logic structure and writing specifications. According to the method, the fragmented expression problem is effectively avoided, the academic value and reading experience of the text are remarkably improved, the inherent defects of a traditional long text generation technology are practically overcome, and a high-efficiency solution is provided for text creation in the professional field.

System and method generating a summarization of text content, performed in a machine learning neural network large language model (LLM) ensemble. The method comprises inputting text content that includes an unstructured text dataset to a trained baseline LLM. The LLM ensemble includes the trained baseline LLM, a trained classification LLM, and multiple finetuned LLMs. Generating, based on performing natural language processing tasks, a baseline summary of the text content based on the trained baseline LLM, and a classification of topics of the text context via the trained classification LLM. Generating respective finetuned LLM summaries of the text based upon inputting the text content to the multiple finetuned LLMs. Determining, based on a semantic similarity analysis, respective text semantic similarity measures across the baseline summary compared to the finetuned LLM summaries. And generating a summarization of the text content for a topic based on the similarity measures.

The invention belongs to the technical field of natural language processing and intelligent knowledge retrieval, and provides an open type question implicit intention recognition method and system based on multi-agent reasoning. According to the technical scheme, multi-language background knowledge is constructed through a multi-language translation agent and an information retrieval agent; based on the constructed multi-language background knowledge, subject analysis is carried out, multi-field expert agent information is generated, and a multi-field expert agent is dynamically constructed; according to the guide problem proposed by the host agent, generating respective implicit intentions and reasoning argument text contents by using the multi-field expert agent according to the guide problem proposed by the host agent; and performing quality evaluation on the implicit intention and the reasoning argumentation text content by utilizing the host agent, and constructing an implicit intention library by taking the speech of each screened expert agent as a node. The defects in the aspects of processing complex semantic understanding and the like are effectively overcome, and the implicit intention in the open type problem is recognized more accurately and comprehensively.