2115 results about "Text generation" patented technology

Enumerated source text passages may be determined based on one or more source text documents. The enumerated source text passages may include source text passage identifiers uniquely identifying the passages. A novel text passage including novel text portions may be determined based on a query and the enumerated source text passages. One or more of the novel text portions may be verified by a large language model to produce text verification information. A novel text generation message including novel text generated by the large language model may be determined based on the text verification information and sent to a client machine.

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention provides an audit report automatic generation method based on natural language processing, and relates to the technical field of natural languages, and the method comprises the steps: obtaining audit document data passing the examination and approval, the audit document data comprising an audit manuscript, an audit problem, an audit evidence obtaining sheet and an audit verification sheet; performing semantic analysis on the audit document data through a natural language processing model, and extracting key information of an audit subject, a risk point and a compliance conclusion; inputting the key information into a structured processing algorithm to generate structured data matched with a preset auditing template; and inputting the structured data into a pre-training text generation model for language reconstruction, generating an audit report text, automatically filling the audit report text into a corresponding field of an audit template, and outputting a complete audit report.

The invention discloses a knowledge graph-based large language model intelligent teaching aid question-answering system and an implementation method thereof. The knowledge graph-based large language model intelligent teaching aid question-answering system comprises a knowledge base module, a question-answering module and a graph visualization module. And the knowledge base module divides the subject resources into documents to be stored as a local knowledge base of a large language model, and performs related vector base construction for the local knowledge base, so that subsequent retrieval enhancement generation tasks are facilitated. The question and answer module converts user questions into embedded vectors, retrieves most relevant documents in a local knowledge base through semantic similarity to serve as context filling of a large language model, and through prompt word engineering construction, the large language model generation effect is enhanced, the professional degree is enhanced, and the illusion problem is reduced. And the graph visualization module returns the context generated by interaction between the user and the large language model to the large language model again, knowledge nodes in the context are extracted through prompt word engineering, so that a context knowledge graph is constructed and visually presented to the user, an inquiry node and an inquiry relationship are set for interaction, and the interaction experience of the user is improved. According to the method, the knowledge graph and the big language model application are combined and landing in the education field, the text generation and semantic understanding ability of the big language model and knowledge graph visualization are utilized, the question and answer enthusiasm of the user is enhanced, and the learning effect of the user is improved. Meanwhile, a novel man-machine collaborative normal form is provided for an education mode, and a new insight is provided for the technical field of education.

Provided in the embodiments of the present disclosure are a dialogue content generation method, a model training method, a prompt text generation method, a device and a storage medium. The dialogue content generation method comprises: receiving an input question; determining whether the question needs to be answered in combination with external knowledge; if the question needs to be answered in combination with external knowledge, acquiring target knowledge from a preset knowledge base on the basis of the question, generating prompt text corresponding to the target knowledge, inputting into a dialogue content generation model the question, the target knowledge and the prompt text corresponding to the target knowledge, and generating dialogue content by means of the dialogue content generation model, wherein the prompt text is used for prompting the dialogue content generation model to answer the question with reference to the target knowledge; and if the question does not need to be answered in combination with external knowledge, inputting the question into the dialogue content generation model, and generating dialogue content by means of the dialogue content generation model, wherein the dialogue content generation model is obtained by means of performing fine tuning on a pre-trained language model.

A method for training a text generation machine learning model for prompt generation includes receiving a plurality of training text strings; executing a text generation machine learning model using each of the plurality of training text strings to generate a plurality of training generated text strings, each of the plurality of generated text strings corresponding to generating a content item; executing a content scoring machine learning model using each of the plurality of generated text strings to generate a text performance score for each of the plurality of training generated text strings; for each of the plurality of generated text strings, adjusting one or more weights or parameters of the text generation machine learning model proportional to the text performance score for the generated text string.

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

The embodiment of the invention provides an intelligent project text generation method and device, and the method achieves the precise screening of positive and negative training corpora through the construction of a multi-dimensional scoring mechanism, and the comprehensive evaluation of the text coverage degree, the technical correlation degree, the fluency and the professional term specification degree. And constructing a complete knowledge graph based on the relation extraction model and the external knowledge base, and fusing the enterprise technical feature matrix and the basic information to generate an enterprise portrait vector. By transforming a decoding layer structure of a pre-training large model, enterprise portrait vectors are used as condition vectors to be embedded, and personalized text generation is achieved. According to the method, the defects of the traditional technology in the aspects of training sample quality evaluation, knowledge graph integrity and enterprise feature fusion are effectively overcome, and the professionality and pertinence of project text generation are remarkably improved.

Disclosed in the present disclosure is a rich-media document auxiliary generation apparatus. The apparatus comprises a material extraction module, a theme sorting module, a semantic retrieval module, a structured data text generation module, an illustration recommendation module and a video composition module. The present disclosure uses intelligent means to assist a user to efficiently generate a high-quality rich-media composite document, thereby quickly and accurately describing a theme event in an all-round way.

The invention discloses an operation and maintenance question-answering system and method based on a large language model workflow, and mainly relates to the technical field of information security. Comprising a question semantic classification module used for performing semantic analysis and classification on questions through a text classification large language model when the questions are input, and identifying categories of the questions; the process selection and execution module is used for selecting and executing a corresponding process according to the semantic category of the question to generate a preliminary answer; and the answer generation and formatting module is used for processing the preliminary answers through a text generation large language model and formatting the preliminary answers into final answers which can be understood by the user. The method has the beneficial effects that the accuracy of operation and maintenance knowledge retrieval can be improved, problems can be converted into operation and maintenance instructions to execute related operations, meanwhile, the method has the capability of analyzing and deciding operation and maintenance problems, and the response quality and the capability of processing complex problems are remarkably improved.

A computer-implemented method and system for responding to user posts containing images with relevant image responses during conversation between a user and a chatbot. The system receives an image post from the user and generates a description of the image using an image-to-text model. User intent is determined based on the image and description. If responding with an image is appropriate based on the user intent, the system generates a prompt using the image description and passes it to a text generation model to create an image description and caption. The image description and caption are used to synthesize a new image. The resulting image and caption are packaged into a post that is provided as a response to the user. The system uses machine learning pipelines and models to analyze images, detect inappropriate content, classify user intent, generate text, and synthesize images.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a protocol text generation and verification method and device, equipment and a medium. The method comprises the following steps: acquiring multi-modal data related to a target object, constructing a dynamic abnormal portrait, matching the dynamic abnormal portrait with a clause knowledge graph, screening candidate clauses to form a candidate clause pool, generating a protocol text according to the candidate clause pool, carrying out logic conflict detection on the protocol text, and carrying out compliance verification when a logic conflict is not detected. And after the compliance verification is passed, generating a record from the protocol text, storing and outputting. According to the method, multi-source clause knowledge and dynamic abnormal portraits are fused, automatic clause screening and protocol generation based on risk features are achieved, logic conflict detection and compliance verification are carried out, the generation efficiency and personalized suitability are improved, and the protocol compliance is enhanced.

The present application is applicable to the technical field of artificial intelligence, and provides a text generation method, a method for training a text generation model, and a related device. The method comprises: acquiring an input text; determining an input token vector on the basis of the input text, wherein the input token vector comprises an input token array, an output token array, an output mask array, N candidate tokens, and N candidate mask arrays; inputting the input token vector into a text generation model to obtain an output result, wherein the output result comprises candidate output tokens and a probability distribution thereof; sampling the output result to obtain a sampling result; performing rejection sampling on the basis of the sampling result to obtain an output token; and adding the output token into the output token array, and using the output token array as an output text. The method in an embodiment of the present application can ensure generation performance while improving decoding speed and does not rely on any additional models.

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.

The invention discloses an image report generation method, system and device based on multi-modal learning optimization, and the method comprises an image retrieval module which is used for carrying out the image feature extraction of an input medical image, and obtaining similar medical images and reports; the simulation generation module is used for guiding an image report generation model to generate an initial draft report by utilizing the medical image and the report; the personalized diagnosis module is used for carrying out personalized adjustment on the report generation model according to the historical report preference of the doctor or the historical medical data of the patient; the reinforcement learning optimization module is used for dynamically adjusting a text generation mode based on a reinforcement learning report optimization strategy and optimizing an image report generation model in combination with doctor scoring, automatic evaluation and clinical feedback; and the iterative refining module is used for performing text retrieval by using the initial draft report as a query, acquiring text fragments which are semantically similar to the initial draft report, and performing neural symbol reasoning optimization to generate a final report.

A method and related system for generating document embeddings within an embedding space based on a set of structured documents by determining (i) a first vector based on a first segment of a first document and (ii) a second vector based on a second segment of the first document and updating association vectors indicating the second segment based on a distance between the first and second vectors. The method also includes generating a document embedding based on the association vectors, generating a candidate vector based on a candidate document, and determining a result indicating that a second distance between the candidate vector and a first document embedding satisfies a document embedding distance threshold. The method may also include generating a new document by providing, to a text generation model, a portion of the candidate document and a portion of the second segment of the first document.

The invention provides a large model text generation method and system based on self-adaptive cue words, and belongs to the technical field of large model text generation. The technical problem to be solved is that the current large language model text generation has the defects of selection deviation and uncertainty. According to the technical scheme, hierarchical clustering processing is carried out on problems contained in a large-scale multi-task language understanding MMLU data set, a text embedding and similarity calculation matrix is adopted, and a plurality of problem clusters containing similar features are constructed and obtained; randomly extracting representative problem examples from each problem cluster, and constructing excellent cue words with remarkable guide effects for each problem based on domain knowledge and potential semantic association; a Qwen model is adopted to generate candidate cue words with diversity and pertinence for various question clusters divided in advance, a cue word bank is constructed, and each question and cue word pair in the cue word bank is manually scored; the method is applied to text generation.

The invention discloses a data analysis problem generation method based on image input and large model combination, and the method comprises the following steps: S1, obtaining original image data, and carrying out the preprocessing of the original image data; s2, constructing a visual language model based on a Qwen-VL architecture, and performing bidirectional alignment to generate a visual feature vector; s3, constructing a cue word template, and fusing the cue word template through a cross attention mechanism to generate structured text description; s4, constructing a large language model based on a Transform architecture, and performing supervision and fine tuning by adopting a LoRA method to generate a data analysis problem candidate sequence; s5, semantic consistency detection and structural rule matching are carried out, and sequences which do not meet semantic specifications or structural constraints are removed; and S6, constructing an image-text alignment triple, and writing the image-text alignment triple into the data structure in the JSON format for coding and storage. According to the method, the image can be converted into a data analysis problem, and the text generation quality and efficiency are remarkably improved.

A query request identifying a query and a plurality of text portions for determining an answer to the query may be received. Relevance scores corresponding with respective ones of the text portions may be determined based on application of one or more machine learning models to the respective text portion and the query. A subset of the text portions may be selected based on the relevance scores. A response message to the query request including an answer to the query in natural language text generated by a large language model based on the first subset of text portions may be determined.

A method for problem inference based on multi-modal generative artificial intelligence includes receiving question information including an image and text, generating formal languages by parsing the image and text of the question information, respectively, based on a pre-constructed problem solving template, generating text-based intermediate inference information for the question information by inputting the generated formal language to a formal language inference unit, generating image-based inference information by inputting the text-based intermediate inference information, the text included in the question information (hereinafter referred to as “text question information”), and the image included in the question information (hereinafter referred to as “image question information”) to a multi-modal image generation model, and generating text-based inference information by inputting the text-based intermediate inference information, the image-based inference information, and the text question information to a multi-modal text generation model.

The invention relates to the technical field of data processing, in particular to a strategy generation and evaluation method based on a large language model, a medium and equipment, and the method comprises the steps: dynamically determining a first model unit and a second model unit through the character number of codes and cue words and the memory attribute of a preset large language model; resource and efficiency balance is optimized while independence of reference strategy codes is guaranteed, and integrity and stability of initial strategy text generation are guaranteed; an initial strategy text is generated according to the first model unit, and reference strategy codes corresponding to the roles are generated in combination with the preset role cues and the second model unit, so that the same initial strategy text can adapt to understanding perspectives of different roles, and a multi-dimensional evaluation basis is obtained; in combination with the first similarity between the strategy codes and the weight of the preset role prompt word, effectiveness evaluation is performed on the initial strategy text, the semantic transfer accuracy and interpretation suitability of the initial strategy text in a multi-role scene are reflected, and meanwhile, the accuracy of an evaluation result is improved.

A neural network audio codec system and related methods are provided. In one example, a method is provided comprising: obtaining speech audio to be encoded; applying the speech audio to an audio encoder that is part of a neural network audio codec system that includes the audio encoder and an audio decoder. The audio encoder and the audio decoder have been trained in an end-to-end manner. The speech audio is encoded with the audio encoder to generate embedding vectors that represent a snapshot of speech audio attributes over successive timeframes of the raw speech audio, and from the embedding vectors, codeword indices are generated to entries in a codebook. The codeword indices are then transmitted or stored for later retrieval and processing by the audio decoder. Further aspects include converting the speech audio to text based on decoding the codeword indices, translating the speech audio from a first language into a second language by mapping codeword indices from a first language to a second language, converting the speech audio prosody by mapping the codeword indices, text generation and discrimination tasks.Yet another aspect includes converting text to speech audio with a different prosody.

The invention provides a text generation method and device based on a large model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence such as large models and natural language processing. According to the specific implementation scheme, a matched prefix is obtained; the matching prefix comprises at least one continuous lexical element; based on the matching prefix, according to a pre-configured draft lemma sequence length, obtaining a draft lemma sequence; the draft lexical element sequence comprises at least one lexical element; performing validity verification on the draft lexical element sequence based on a speculation decoding algorithm by adopting a pre-trained large model; and in response to verification passing, taking the draft lexical element sequence as a generated text. According to the technology disclosed by the invention, the text generation efficiency of the large model can be effectively improved.

The invention discloses a multi-agent-based programmable logic controller structured text generation method. The method comprises the following steps: constructing a programmable logic controller programming task solution knowledge base; five large model agents are constructed, including a knowledge retrieval agent, a task planning agent, a structured text generation agent, a verification agent and an error correction agent. According to the method, through mutual cooperation of multiple agents and flexible calling of various tools and models, the knowledge deficiency of a large language model in the programming aspect of the programmable logic controller is made up by utilizing a retrieval enhancement generation technology, the illusion problem of the large language model in the process of generating the structured text of the programmable logic controller is effectively reduced, and the reliability of the structured text of the programmable logic controller is improved. And the success rate and the expansibility of automatically generating the structured text of the programmable logic controller are improved.

The invention relates to a zero sample text classification method and device based on cross-modal information completion. According to the method, cross-modal information complemented image construction is realized by designing a new image label mapping mechanism and based on context text generation, finally, a multi-modal collaborative reasoning framework is designed in a reasoning stage, text semantic perception capability is enhanced by prompting project optimization text input, and zero sample reasoning capability of a CLIP model is effectively improved. Aiming at the semantic deviation problem of automatic label generation, a cross-modal information complemented image is constructed based on context text generation so as to optimize the text semantic representation integrity, and meanwhile, a prompt design is introduced in a reasoning stage to enhance the text semantic perception capability; according to the two-stage keyword automatic selection mechanism, firstly, a keyword candidate set is generated through a large model, secondly, through multi-modal matching and selection, the optimal keyword phrases are selected to serve as text information complemented by cross-modal information, and semantic accuracy and field adaptability are effectively improved.

The invention discloses a text-driven advertisement video generation method based on emotion feedback, relates to the technical field of digital advertisement and man-machine interaction, and aims to dynamically adjust advertisement content and improve individuation, interactivity and conversion rate by collecting emotion data such as facial expression, voice features and eye movement tracks of a user in real time. A text generation video model and a multi-mode emotion recognition technology are combined, and feature fusion and feedback processing are utilized, so that the advertisement video better conforms to the emotion of the user; multi-round feedback and self-adaptive optimization ensure that advertisements transmit core information and flexibly respond to user emotional states at the same time, attraction and participation degree are greatly improved, an innovative feature fusion algorithm further improves individuation and real-time response capability of generated videos, a brand new solution is provided for the field of digital advertisements, and the method is suitable for popularization and application. And the development of individuation and emotional interaction is promoted.

The invention provides a big language model pruning method and device, a storage medium and a program product. The method comprises the steps of obtaining a pre-trained big language model and a corresponding text data set; dependent neurons are activated through iteration, interdependent coupling groups in the large language model are recognized, and the coupling groups serve as basic units of pruning; based on the coupling group, performing coarse-grained evaluation and fine-grained evaluation on the parameters in the group, and fusing and calculating the importance of the parameters to generate a pruning mask; redundant parameters in the model are removed through pruning operation according to the pruning mask; performing parameter optimization on the pruned model, and performing fine tuning through a text data set to generate a target large language model; and deploying the target large language model to a computing device, executing a text generation or semantic analysis task, and counting the task accuracy rate and the model compression rate. According to the method, the parameter quantity of the model is effectively reduced, so that deployment is simple, and the downstream task processing accuracy of the pruned model is improved.

The invention provides an AI auxiliary reading method and system based on dynamic partitioning knowledge classification self-verification, and relates to the technical field of artificial intelligence, the method comprises the following steps: carrying out dynamic semantic partitioning on a super-long text to obtain a plurality of initial text units with consistent internal semantics; performing topic model construction according to the initial text unit and a layered Dirichlet process to obtain a topic distribution tag; receiving a user query request, and extracting a retrieval text set according to the hierarchical retrieval strategy and a retrieval text corresponding to the user query request; inputting the constraint cue word extracted by the topic distribution tag corresponding to the retrieval text set, the retrieval text set and the retrieval text into a text generation model to output a to-be-calibrated text; if the error scoring function of the to-be-calibrated text and any initial text unit in the retrieval text set is smaller than a preset error threshold value, the to-be-calibrated text serves as a credible text to be output to a user. According to the invention, the credibility and accuracy of the answer can be enhanced.

The invention discloses an image counterfeiting multi-type detection method and system based on a visual large language model, and belongs to the technical field of image processing and computer vision. The method comprises the following steps of: preprocessing a data set containing normal, copy and paste forged and spliced forged images, extracting visual and language features by utilizing a pre-training model, and realizing feature fusion by adopting a multi-modal alignment technology. Whether an image is forged or not is judged through a classification task, a forged area is positioned through a segmentation task, and meanwhile a forged text description is output through a text generation module. The loss function is optimized in combination with classification, segmentation, comparison and text cross entropy loss. And adopting an end-to-end training and course learning strategy training model, and outputting a forgery probability, a regional mask and text description during reasoning. According to the method, multiple counterfeit types can be processed at the same time, high-precision positioning is achieved, multi-modal information is effectively fused, and detailed counterfeit text description can be provided.