205 results about "Word generation" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a cue word compiling and generating method and system for a large language model.The method comprises the steps that in response to a compiling calling request, a PDL protocol is received to serve as a compiling input text; performing lexical and grammatical analysis on the compiled input text, and constructing a corresponding abstract syntax tree AST as an intermediate representation according to a context-independent method; semantic verification and structure correction are carried out on the AST, and when semantics are effective, structure optimization is carried out to generate an optimized AST; and traversing the optimized AST, matching a cue word template in a cue word generation rule base, mapping semantic nodes into structured text fragments, splicing the structured text fragments to form a final cue word text, and outputting a compilation result consistent with PDL protocol semantics. By introducing a PDL-oriented compiling mechanism, automatic analysis, structure optimization and high-quality cue word generation of an input protocol are realized before cue words of a large language model are generated, and the development efficiency and the output quality of the cue words are improved.

The invention relates to the technical field of natural language processing, in particular to a context-based Prompt generative model optimization system, which comprises a context analysis module, a cue word generation module, a context optimization module, a semantic check module and a structure reconstruction module. According to the method, the context path and the semantic hierarchy information of the semantic unit are introduced, the fine degree of semantic matching degree recognition is improved, semantic guide deviation caused by statement template solidification is avoided, the cue words are recombined in combination with the semantic coherence weight and the logic dependency relationship, and the recognition accuracy is improved. The consistency and expression accuracy of the prompt content in the context are enhanced, the prompt word insertion sequence and connection mode are dynamically adjusted through a semantic conflict detection and structure rechecking mechanism, coherence and stability of a semantic structure and controllable generation of the prompt content are kept, semantic conflicts and expression chaos caused by static matching are effectively avoided in the generation process, and the generation efficiency is improved. And dynamic adaptation of prompt configuration and smooth optimization of language output are integrally realized.

The invention discloses a game cue word generation method fusing an expert strategy and multi-thinking-chain reasoning. The method comprises the following steps that multi-modal input composed of a text and an image is obtained; multi-mode information is injected into multi-thinking-chain reasoning and expert strategies, and efficient access and cue word splicing of the multi-mode information are achieved; inputting the micro-operation multi-mode cue word fusing the expert multi-stage strategy and the multi-thinking chain reasoning into a large language model; performing comprehensive analysis on the input information by the large language model, generating a plurality of thinking chains in parallel, selecting an optimal scheme through evaluation, and mapping a high-level decision into a specific game operation instruction; the large language model outputs an action instruction to an LLM-PySC2 interface, and the game environment is executed and influenced; and outputting game information, related knowledge, legal actions, action parameters and actions of the previous step back to the large model, and carrying out learning and evaluation again.

The invention discloses a multi-agent cooperative cue word engineering optimization method, system and device and a medium, and relates to the technical field of cue word engineering, the method comprises the following steps: after initially receiving a user input task, starting a cue word optimization process; an initial cue word template is created through a cue word generation agent, historical optimal templates are retrieved from a knowledge base for fusion, and candidate cue word templates are generated. And the analysis agent inputs the candidate template into the second large language model to generate a candidate answer. And the evaluation agent performs multi-dimensional evaluation on the answers and gives scores and defect analysis. And according to an analysis result, updating the cue word to generate a candidate template of the intelligent agent. And arranging the intelligent agent to cyclically execute the steps until the stop condition is met, and outputting the optimal cue word template. And finally, storing the optimal template into a knowledge base and establishing a semantic index to support subsequent retrieval reuse. According to the method, manual intervention is not needed, and a complete automatic process of generating, evaluating and optimizing the cue word is realized.

The embodiment of the invention provides a method for constructing large model cue words in the financial credit field based on the ReAct theory. The method can solve the technical problem of cue word errors caused by semantic understanding deviation in related technologies. The method comprises the following steps: defining a role and a capability boundary of a dynamic cue word generation model, and binding a structured credit knowledge graph; inputting the received original business problem, the scene label and the real-time data into a dynamic prompt word generation model to obtain at least one first keyword; matching entity nodes related to the first keyword in the credit mapping knowledge domain, and generating a keyword entity association result; calling a preset process template according to the scene label, generating a reasoning chain comprising at least one reasoning layer node, and inserting a judgment condition: binding a tool calling instruction at the action layer node, obtaining external data, backfilling the external data to the reasoning chain, obtaining backfilled data, executing the reasoning chain, and outputting a service result; the cue word priority weight is improved according to the service result, and the cue word accuracy is improved.

The invention discloses a low-altitude flight intelligent decision-making method based on atlas retrieval enhanced generation and digital flight rules. The method comprises the following steps: firstly, preprocessing and translating a digital flight rule (DFR) text, extracting entities and relationships through a large language model, constructing a structured knowledge graph, and storing the structured knowledge graph in a vector database; and secondly, circularly calling the large model for three times: calling an optimization cue word to generate a template for the first time, calling a template-based analysis DFR text to complete atlas construction for the second time, and calling a combination atlas retrieval capability to execute compliance verification and route intelligent decision-making of a low-altitude flight task for the third time. In addition, when an airspace rule is changed or a temporary announcement exists, real-time updating of knowledge is achieved by inserting a new node into the knowledge graph or modifying edge attributes. According to the method, GraphRAG and DFR combination is generated through map retrieval enhancement, the interpretability, accuracy and dynamic adaptability of large model decision making are improved, and the problem of complex decision making in a low-altitude environment is solved.

The invention relates to the technical field of financial science and technology and natural language processing, in particular to an intelligent financial cue word generation and optimization system based on a knowledge graph, which comprises a financial field knowledge base, a cue word element extraction module, a field specialization template library, a cue word generation engine, an evaluation and optimization module and a manual feedback and labeling system, the efficiency is improved, through template generation and parameterization configuration, the prompt word writing time is shortened, and the labor cost is remarkably reduced. And quality guarantee: the accuracy of professional terms is improved, the structural integrity and compliance coverage are remarkably improved, and the output deviation of a large model is reduced. And scene adaptation: the fields of security investment, financial analysis and the like are supported, and the cue word complexity and professional depth are automatically adjusted according to user roles (investors / analysts). And dynamic evolution: through real-time updating of the knowledge graph and artificial feedback learning, the system can adapt to financial environment changes (such as new supervision policies and emerging analysis methods), and continuously optimizes and generates strategies.

The invention discloses a large language model context capability excitation method and system based on an attention sink point. The method comprises the following steps: firstly, extracting a key context information vector from input data; then, in some specific Transform decoding layers of the model, anchor lexical elements in the input sequence are identified, and attention calculation paths of the anchor lexical elements are separated. In the anchor point path, contextual information is injected into representation of anchor point lexical elements through one-time cross attention calculation, and the result is stored in a key value cache for being reused in the subsequent generation step; and the non-anchor lexical elements keep the original calculation mode. And finally, the model carries out autoregression lexical element generation based on the updated sequence representation until a complete text is output. As a universal module, the method can be seamlessly integrated to any pre-training large language model based on a Transform decoder, and context information of the model is effectively excited and utilized on the premise that extra reasoning calculation overhead is hardly introduced.

The invention discloses a sensitive cue word generation method and system based on parameter sensitivity quantification, relates to the technical field of artificial intelligence security, and aims to solve the problem that sensitive test samples are difficult to generate in integrity verification of a black box large language model. The method comprises the steps of obtaining an original large language model; a comprehensive parameter sensitivity index is constructed, the microscopic sensitivity used for representing tiny parameter modification and the macroscopic sensitivity used for representing large modification such as parameter quantification and pruning are fused, and therefore the sensitivity of cue words is comprehensively quantified; in the continuous embedding space, a gradient optimization algorithm is adopted to maximize the comprehensive index as a target to carry out iterative optimization, and constraints such as semantic rationality are applied to ensure that the generated cue word is natural and smooth; and finally, mapping the optimized embedded vector back to the discrete lexical element sequence to obtain a final sensitive cue word. According to the method, the low-cost and automatic test sample generation is realized, and the accuracy, efficiency and concealment of cloud model integrity verification are remarkably improved.

The invention provides a cue word generation method, computer equipment and a computer program, and the method comprises the steps: obtaining first reply information which is generated by a target model, is used for output and corresponds to first question information, carrying out the blocking processing of the first reply information, and carrying out the caching of a plurality of pieces of first segment information; in response to second question information which is input by the user and belongs to the same dialogue scene with the first question information, retrieving target fragment information associated with the second question information in the multiple pieces of first fragment information, and based on the second question information and the target fragment information, according to a cue word template used for guiding a target model; and creating a cue word corresponding to the second question information to assist the target model in understanding target fragment information required by the context of the dialogue scene so as to generate second reply information. The processing process can be realized by a computer program based on artificial intelligence, namely an intelligent agent, and the intelligent agent can be embedded into an application degree or an operating system such as a browser or an application store to provide an intelligent function in a dialogue form.

The embodiment of the invention provides a cue word generation method and device, a program product and a storage medium, and the method comprises the steps: obtaining task description information of a target task; performing prompt word generation operation on the task description information by using a pre-trained target network model to obtain a target prompt word; wherein the generation operation comprises the steps that multiple meta prompts are extracted from the task description information, a meta prompt set is obtained, and the multiple meta prompts all comprise the summary of the target task; target mapping operation is executed on the meta prompt set to obtain a candidate prompt word set comprising a plurality of candidate prompt words, and the target mapping operation comprises construction of a mapping relation among the plurality of candidate prompt words and a mapping relation between the plurality of candidate prompt words and preset candidate prompt words; and extracting a target cue word of which the quality feature is greater than a preset threshold value from the candidate cue word set. Through the method and the device, the problem that accurate cue words cannot be generated in related technologies is solved.

The invention discloses a model answer generation method and device, equipment and a storage medium, and relates to the technical field of large model application, and the model answer generation method comprises the steps: obtaining a user question of a target user; inputting the user question into a user question and answer model for intention recognition, cue word generation and answer generation to obtain a model answer output by the user question and answer model; and pushing the model answer to the target user to complete user question and answer. According to the method and the device, the requirements of the user can be quickly and accurately understood, so that an accurate direction is provided for subsequent prompt word generation and answer generation, answer deviation caused by misunderstanding of the intention of the user is avoided, personalized prompts can be generated according to the questions of different users, the answers better meet the specific requirements of the user, and the user experience is improved. The pertinence and practicability of the answers are improved, so that the user question-answering model can generate higher-quality and more accurate answers, and the trust and satisfaction of the user to the question-answering system are enhanced.

The invention discloses an intelligent cue word generation evaluation method and system based on multi-component collaboration, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: acquiring a multi-format text, converting the multi-format text into an input-output pair, and generating a plurality of candidate cue words; after determining a communication mode according to the text form, standardizing cue words according to the structured template of each large language model; calling each platform to generate answer analysis and unify formats, and extracting target contents by utilizing regularization; and carrying out multi-mode evaluation on the candidate cue words, selecting an optimal cue word, and outputting a structured evaluation result and an optimization suggestion based on multiple indexes. According to the method, full-process automatic processing of prompt word generation, optimization and evaluation is achieved, various model platforms, data sets, algorithms and evaluation indexes are supported, and good universality and expansibility are achieved.

The invention discloses an underground engineering on-demand ventilation knowledge graph retrieval enhancement generation method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps of data cleaning and ontology layer modeling, knowledge extraction based on prompt engineering, knowledge graph data filling and storage, storage of an extraction result in a Neo4j graph database, and retrieval enhancement generation of an inference engine. Developing an intelligent question and answer platform, and providing knowledge graph visualization and interactive question and answer functions; the system comprises a data acquisition and preprocessing layer, an ontology and knowledge extraction layer, a graph storage layer, a semantic index and entity link layer, a sub-graph arrangement and cue word generation layer, a generation and reasoning layer and an application and interaction layer. According to the invention, the acquisition and management efficiency of underground engineering ventilation knowledge is improved, the professionality and accuracy of the question-answering system are enhanced, the interface is friendly, knowledge tracing is supported, and convenient experience is provided for users.

The invention provides a dynamic interactive document generation method and device based on generative AI, computer equipment and a storage medium. The method comprises the following steps: receiving multi-modal input data; generating a composite cue word by adopting a cross-modal semantic alignment model of a QWen text encoder and a BriVL image encoder; dynamically adjusting the semantic weight of the prompt word based on a weight formula of a knowledge graph matching degree, a user historical acceptance degree and an AI confidence degree; calling the generative AI model to generate document content; a quality detection module identifies questions and actively inquires a user by adopting a layered interaction strategy; correcting the content through an incremental cue word according to the feedback; and optimizing a cue word generation strategy by adopting a PPO reinforcement learning algorithm. According to the method, the problems of static template limitation, low efficiency of cue words and lack of interactivity in traditional document generation are solved, dynamic and interactive personalized document generation is realized, and the document generation quality and efficiency are remarkably improved.

The invention discloses a Text2SQL (Structured Query Language) system and method based on cue words. The system comprises a natural language input module for receiving and preprocessing a natural language query statement; the metadata management module is used for managing database metadata information; the cue word generation module is used for generating cue words containing database modes, user problems and SQL requirements on the basis of the query statements and the metadata; the large language model module is used for generating a preliminary SQL based on the cue word; the SQL grammar verification module is used for verifying grammar and feeding back errors; the manual auditing module is used for auditing and editing the SQL statements passing the verification; and the SQL execution module is used for executing the SQL passing the audit and returning a result. According to the method, the accuracy is improved by combining cue word guidance and a large model, the field adaptability is enhanced by relying on metadata, the data security is ensured by introducing manual auditing, the accuracy and security of SQL generation in a complex query scene are remarkably improved, and the development and maintenance cost of the system is reduced.

The embodiment of the invention provides a large model cue word generation method and device, a storage medium and a program product. In the method, quality index analysis can be performed on the initial cue word based on multiple task dimensions, so that iterative optimization is performed on the initial cue word accurately and efficiently based on the analysis result, the obtained reference cue word is matched with the task target of the model task on multiple task dimensions, and the effect is better. The reference cue word is derived, a cue word list of the target large model can be obtained, the cue word list comprises a plurality of candidate cue words, and different candidate cue words perform the same task guidance on the target large model through different description styles and / or cue word contents. According to the method, the user can select the target cue word as required when calling the target large model, so that the diversity of the cue word is enriched, the cue word list adapts to different description styles and content requirements, and the flexibility and applicability of calling the large model by the user are improved.

The invention relates to a word generation method and device, electronic equipment, a non-transitory storage medium and a computer program product. The word generation method comprises the steps that a query vector Q of a previous generated word of a current word to be generated and keys K and values V of a plurality of generated words are read from a universal flash memory (UFS); based on the query vector Q and the key sum value of each generated word, calculating the importance value of the generated word; selecting a target generated word based on the importance value of each generated word; loading keys and values of the target generated word from the UFS into a memory; a word is generated using the key K and the value V of the target generated word loaded into the memory.

The invention discloses a digital main line automatic modeling method and system based on a large language model, and the system comprises a database system, an embedded model, a vector database, a matching processing unit, a front-end interface, a cue word generation module, and a large language model interface. The digital main line automatic modeling method based on the large language model comprises the following steps: S1, extracting data information of a standard database and a database to be matched and converting formats; s2, storing the data information of the standard database in a vector database in a vectorization manner by using an embedded model; s3, vectorizing to-be-matched business database data information, searching similar fields in a vector database, and selecting candidate fields; s4, the user checks the candidate similar fields and confirms the mapping relation; s5, the data matching relation is input into the large language model, and a data transfer SQL is generated; and S6, executing SQL to realize data integration and automatic modeling.

The invention provides a business rule cue word generation method and device, electronic equipment, a medium and a program product, relates to application of a large model in a financial science and technology scene, and can be applied to the technical field of artificial intelligence and the technical field of big data. The method comprises the steps of obtaining business demand information submitted based on a target interactive interface, and converting the business demand information into structured demand data; generating an initial cue word based on the structured demand data; inputting the initial cue word and the test data into the large model to obtain a first output result; comparing the first output result with a corresponding expected label, identifying a difference sample, and generating a cue word variant based on the difference sample; and utilizing the cue word variant to drive the large model to carry out iterative verification on the test data until the verification performance meets a preset performance condition, and outputting the service rule cue word.

The invention discloses a titanium alloy multi-modal data extraction method based on a Deepseek large model, and belongs to the technical field of material science and data processing. The titanium alloy multi-modal data extraction method based on the Deepseek large model solves the technical problems that an existing titanium alloy database is incomplete in data, and multi-modal data are difficult to accurately extract and utilize, and comprises the following steps that S1, titanium alloy literature collection is carried out; step S2, data standardization; a standard term table is set up, and the unit format of each entry is stipulated; s3, generating and optimizing prompt words; according to a target extraction task, an initial cue word is selected and optimized by referring to the standard term table in the step S2; s4, data batch extraction: performing data batch extraction on the file acquired in the step S1 on the basis of the optimal prompt word acquired in the step S3; and S5, data storage. According to the method, the efficiency of data extraction and management is remarkably improved, and a complete data support is provided for research and development of titanium alloy materials.

The invention discloses an insurance short video content generation method and device, a medium and a program product, and relates to the technical field of artificial intelligence. Semantic analysis is performed on hot event data to extract core risk elements, so that the system can capture risk features concerned by the current society. The core risk elements are matched with the guarantee demands of the potential users, the guarantee gap is determined, and the association mapping between the hot event risks and the guarantee demands is established, so that the determination of the target insurance product has risk guidance. And determining a cue word generation strategy from a preset strategy library and inputting the cue word generation strategy into the insurance field large language model to generate a first draft of the content, thereby realizing automatic generation of the content. And in combination with a preposed compliance detection mechanism, the compliance of the generated content is ensured. Quick response and generation of the insurance short video content are realized, and the content generation efficiency is improved.

The invention provides a bridge operation and maintenance knowledge management and question answering method based on a knowledge graph and a large language model. The method comprises the following steps: performing semantic dicing and dynamic cue word generation on texts such as a design report and an overhaul report through a large language model, automatically extracting an entity-relationship-attribute triple, completing entity co-reference resolution and relationship combination, and constructing an initial knowledge graph; further utilizing a curiosity algorithm to continuously merge low-similarity new knowledge in a subsequent detection report to realize iterative growth of the atlas along with an operation and maintenance life cycle; forming a multi-level community by adopting Leiden clustering, generating an abstract pyramid, and enhancing macroscopic semantic expression; and finally, through a semantic embedding model and a GraphRAG technology, precise matching and traceable answering from natural language questions to graph knowledge points are realized. According to the method, the problems of knowledge fragmentation, update lag and illusion in the bridge operation and maintenance field are solved, and the accuracy and robustness of operation and maintenance knowledge questions and answers are improved.

The embodiment of the invention provides a database exception analysis method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: when an abnormal index is detected, determining at least one causal path from an abnormal index knowledge graph, inputting the abnormal index and the causal path into a cue word generation model, and dynamically optimizing cue words through a reinforcement learning network in the cue word generation model, and the optimal prompt word which is more suitable for the current abnormal scene is obtained. Wherein the causal path comprises at least one related index of the abnormal index, obtaining current index data corresponding to the abnormal index and the plurality of related indexes, and further performing root cause reasoning based on the plurality of current index data, the causal path and the optimal cue word, thereby further considering a complex and changeable database operation environment. Therefore, a more accurate and comprehensive root cause analysis report is obtained.

The embodiment of the invention provides a wordart generation method and device, electronic equipment and a storage medium, belongs to the technical field of image processing, and is suitable for the field of finance. The method comprises the steps of obtaining an original font, and performing wordart generation on the original font through a preset wordart generation model to obtain a predicted wordart; performing style loss value calculation according to the original font, the predicted wordart and a preset reference wordart to obtain a style loss value; performing structure loss value calculation according to the predicted wordart and the reference wordart to obtain a structure loss value; performing parameter optimization on a preset wordart generation model according to the style loss value and the structure loss value to obtain a target wordart generation model; and obtaining a target font, and performing wordart generation on the target font through the target wordart generation model. According to the embodiment of the invention, the wordart capable of reflecting different calligraphy styles can be generated.

The invention discloses an on-duty document generation system and method based on a large reasoning model, and the system comprises an intention obtaining module, a knowledge base module, an interaction generation module and a style conversion module: the intention obtaining module is used for obtaining a task requirement text, an extension mark and a retention mark; the knowledge base module is used for storing a preset business knowledge text and performing retrieval processing based on a to-be-retrieved text input externally to obtain a retrieval result; the interaction generation module is used for storing a preset expert template set and performing prompt word generation and reasoning based on the task requirement text, the extension mark, the retention mark and the retrieval result to obtain a target on-duty document; and the style conversion module is used for performing style conversion processing on the initial on-duty document based on a preset style template text to obtain a target on-duty document. According to the method, the richness and integrity of the generated on-duty document can be improved, and the personalized on-duty document conforming to industrial characteristics is generated.

The invention discloses a multi-modal dialogue emotion recognition method and device, and relates to the technical field of multi-modal emotion recognition. More accurate emotion prediction is realized through the steps of extracting multi-modal features, inferring an implicit relationship, constructing a mixed relationship graph, performing multi-modal semantic alignment and the like. The method comprises the steps of performing single feature vector extraction and fusion on an input multi-modal dialogue sequence needing emotion recognition to obtain an initial multi-modal feature vector, determining an implicit association relationship between sentences included in each modal according to a constructed similarity function, and performing emotion recognition on the multi-modal dialogue sequence according to the implicit association relationship. And mapping the representation subjected to weighted fusion with the visual and audio modal information to a word list through a linear network to obtain a predicted word generation probability of the target sentence, and determining a difference loss value between the predicted word probability and an actual word probability based on a cross entropy loss function to obtain a loss function.

The invention discloses a cue word generation method and device, equipment and a storage medium, and relates to the technical field of information processing, and the method comprises the steps: obtaining a natural language instruction and an application scene input by a target user, analyzing the natural language instruction, and extracting core intention information corresponding to the natural language instruction; matching and embedding the core intention information with a financial knowledge graph, determining related domain knowledge, dynamically fusing the core intention information, the application scene and the related domain knowledge with a multi-dimensional template, and generating an initial cue word through a cue word generation model; and performing illusion detection, fact error and logic correction on the initial cue word to generate a target cue word. According to the method, intention analysis, cue word fusion and illusion correction combined with the application scene are automatically completed in the whole process, manual intervention and optimization are not needed, the cue word generation period is shortened, and the calculation power consumption and the use cost are reduced while the cue word accuracy is improved.

The invention provides a digital person photo changing method and device, and relates to the technical field of photo changing, and the core of the method lies in that 1, a clothing feature extraction technology is adopted, a visual encoder model is utilized to carry out high-precision feature extraction on a clothing image, and a semantic segmentation technology is combined to realize automatic and precise shielding of a clothing area; according to the dynamic redrawing technology, natural adaptation and detail consistency control of the garment contour are achieved through attitude estimation and a low-rank adapter mechanism; and a context-aware semantic segmentation technology is provided, so that the clothing area can be accurately positioned under a complex background, and the mask accuracy is improved. In addition, a visual language model is introduced, through accurate garment prompt word generation, the mask effect of the segmentation model is further optimized, and the reduction degree of garment details is improved.

A method, apparatus and system for determining question-answer pairs for finetuning a language model includes, for at least two layers of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, and determining at least one question-answer pair intended to increase a semantic understanding of content based on a question generated using at least one word of the set of words and the content to which the question-answer pair is applied. A language model can then be finetuned using the determined question-answer pairs.