142 results about "Context learning" patented technology

The invention discloses a civil administration service question and answer method based on a large model and knowledge graph retrieval enhancement, and the method comprises the steps: S10, inputting a user question, and carrying out the question analysis and entity recognition; s20, the problem complexity is judged, if the problem is a single-hop problem, knowledge graph single-hop retrieval is carried out, and if the problem is a multi-hop problem, knowledge graph multi-hop retrieval is carried out; s30, performing semantic matching sorting to generate sub-answers; and S40, based on the sub-answers and the user question, performing synthesis to generate a final answer. According to the method, the structured knowledge of the knowledge graph and the natural language processing capability of the large language model are fused; a question decomposition module is used to enhance the interpretability of multi-hop information retrieval and answers; and using contextual learning (ICL) and thinking chain (CoT) prompts to generate an individually processed explicit inference chain to improve authenticity; the defects of traditional civil administration service questions and answers in the aspects of knowledge accuracy, reasoning ability and interpretability are overcome.

An example operation may include one or more of storing a table comprising a plurality of columns corresponding to a plurality of attributes and a plurality of rows corresponding to a plurality of records, receiving a target record of a task of an artificial intelligence (AI) model, converting the plurality of records into a plurality of embeddings in multi-dimensional vector space, converting the target record into a target embedding in the multi-dimensional vector space, identifying a subset of records from among the plurality of records that are nearest to the target record in content based on distances between embeddings of the subset of records and the target embedding within the multi-dimensional vector space, and executing the AI model on the subset of records to generate a predicted output with respect to the task.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

A test question recommendation method based on large language model adaptive multi-level evaluation constructs a multi-level architecture including semantic consistency verification, fine-grained fact alignment and dynamic cognitive evaluation, and comprises the following steps: constructing an initialized data stream and executing dynamic random sampling; calculating discrete semantic entropy by using NLI bidirectional implication clustering so as to quantify and eliminate illusion content with high uncertainty; under the RAG framework, the test questions are deconstructed into atomic statements, and a fact deviation is corrected by calculating a retrieval relevance vector and a logical implication consistency score; analyzing and screening low-quality texts based on multidimensional language features of syntax and logic; vector fusion is carried out on the generated intention and the cognitive portrait of the student, and a dynamic evaluation index system adaptive to a specific teaching scene is constructed in real time by utilizing context learning. The problems of accuracy and adaptability of automatic question setting are effectively solved, and intelligent closed-loop control from test question generation to cognitive alignment is realized.

The invention provides a medical intelligent causal decision-making and scheduling system based on an improved large language model, and the system comprises a pre-training language model module which is used for processing unstructured medical text data, and extracting a text feature vector and a medical entity relationship; the knowledge graph enhancement module is used for constructing a medical knowledge graph based on the medical entity relationship, and reasoning to generate a knowledge enhancement feature vector; the multi-modal feature fusion module is used for generating a fusion feature vector based on the text feature vector and pre-stored structured data; the dynamic time sequence modeling module is used for generating a time sequence prediction vector based on the fusion feature vector and the knowledge enhancement feature vector; the self-adaptive scheduling optimization module is used for making a medical scheduling decision based on the time sequence prediction vector and generating a scheduling scheme; and the scheduling calibration module is used for matching similar case features for the scheduling scheme through a context learning mechanism based on the knowledge enhancement feature vector and calibrating the similar case features to generate an optimized scheduling scheme. And medical resources and scheduling can be allocated more accurately.

The invention discloses a medical insurance field document intelligent question-answering system and method based on an AI large model, and belongs to the technical field of artificial intelligence. The technical problems that an existing medical insurance question-answering system depends on manual configuration and is poor in flexibility and low in intelligent level are solved. According to the technical scheme, the system comprises a document processing module for analyzing and segmenting original documents such as medical insurance policy documents, vectorizing the original documents and storing the original documents into a vector database; and the knowledge question and answer module is used for analyzing user questions by using an AI large model, driving AIAgent to retrieve related knowledge in a vector database, performing reasoning in combination with injected professional knowledge such as business dictionaries and analysis logic, and generating accurate and traceable answers. Through context learning, logical reasoning and automatic generation ability of a large model, tedious intention model training and manual QA configuration are not needed, the intelligent level, adaptability and user experience of a medical insurance question-answering system are remarkably improved, and the operation and maintenance cost is reduced.

The embodiment of the invention relates to the technical field of large model data analysis, and discloses a large model evolution multi-agent investment decision matrix system. The investment Transform module is used for acquiring financial data and performing feature extraction and feature fusion on the financial data in combination with a context learning enhancement function and a financial domain knowledge enhancement function; the interpretability analysis module is used for acquiring the feature representation in the investment Transform module and carrying out feature relation mapping and interpretability analysis; the multi-agent decision-making module is used for carrying out collaborative decision-making through multiple agents; the optimization module is used for providing interpretation optimization for the interpretability analysis module and providing strategy optimization for the multi-agent decision-making module; and the knowledge base module is used for providing an investment template for the investment Transformer module, providing causal relationship knowledge for the interpretability analysis module and providing rule constraints for the multi-agent decision-making module. The technical problem of investment decision analysis can be at least solved.

Systems and methods are disclosed that generate dense blob representations such as blob parameters and blob descriptions, and use the dense blob representations to generate images. For example, embodiments of the present disclosure may decompose a scene into visual primitives (e.g., dense blob representations) and based on the blob representations, embodiments of the present disclosure develop a blob-grounded text-to-image diffusion model (BlobGEN) for compositional generation. For example, in some embodiments, a new masked cross-attention module may be introduced to disentangle the fusion between blob representations and visual features. In some embodiments, to leverage the compositionality of large language models (LLMs), a new in-context learning approach may be introduced to generate blob representations from text prompts.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing in-context learning using a generative neural network. In one aspect, a method comprises obtaining a plurality of demonstration examples for a task; obtaining a respective difficulty classification for each of the demonstration examples; generating a context input that includes one or more instances of at least a subset of the demonstration examples, the generating comprising, for each of the demonstration examples, determining how many instances of the demonstration example to include in the context input based on the respective difficulty classification for the demonstration example; receiving a new input for the task; and processing an input that includes the context input and the new input using the first generative neural network to generate a new output.

The invention belongs to the field of intelligent workshop scheduling, and particularly discloses a large model enabled workshop scheduling end-to-end self-decision engine, method and device. According to the method, the large model is positioned as a core decision maker instead of a traditional auxiliary tool, so that the powerful capabilities of the large model in the aspects of natural language understanding, context learning, complex logical reasoning and content generation are fully utilized; according to workshop real-time state information, a dynamically changing workpiece pool, a scheduling target input by a user through a natural language and emergency description, an autonomous and end-to-end scheduling decision can be directly carried out, so that the rapid response capability and intelligent processing level of a scheduling system to dynamic events are improved, and the scheduling efficiency is improved. And the application threshold is reduced through natural language interaction, the dependence on explicit programming and rule customization is reduced, a low-code development paradigm is developed, and the method has a far-sighted decision potential exceeding a traditional heuristic rule.

The invention discloses a method, a system and a medium for understanding and identifying network moduli based on a multi-modal large model, and relates to the technical field of artificial intelligence, a to-be-processed multi-modal task is input into a trained context example selection model, and a context prompt example used for improving the context learning ability of the multi-modal large model is output; the training process of the context example selection model is as follows: performing feature extraction and modal alignment on a multi-modal task sample to obtain cross-modal joint embedding representation; performing coarse-grained sorting on a preset candidate sample library by using cross-modal joint embedding representation, and constructing an initial candidate set; performing fine-grained sorting on the initial candidate set, constructing a nested prompt template based on the obtained knowledge extraction example, and guiding a multi-modal large model to execute a classification reasoning task by taking the nested prompt template as a context prompt example; according to the method, the harmful content recognition precision and the cross-modal reasoning capability of the multi-modal large model in a low-resource scene are remarkably improved.

Aspects of the disclosure are directed to methods, systems, and non-transitory computer readable media for automatically generating queries on a database from natural language text using in-context learning to leverage zero-shot and few-shot adaptation capabilities of large language models (LLMs). The methods, systems, and non-transitory computer readable media can consider database information, employ execution based consistency decoding, and employ a mixture of prompts and / or LLMs.

The invention provides a large model context learning machine translation method based on word granularity alignment, which relates to the field of natural language processing, and comprises the following steps: an external knowledge assisting stage: performing multi-level retrieval matching on a source text word alignment set; a large model translation stage: obtaining a large model translation set, and taking the large model translation set as one of candidate translations; in the post-selection stage, the obtained source text word alignment set, the obtained external dictionary word alignment set, the obtained entity library alignment set, the obtained embedding representation of the auxiliary translation set and the obtained large model translation set are subjected to similarity calculation scoring for multiple times, and screening is conducted according to similarity scores to obtain a candidate word alignment set; a prompt template is designed according to a task, a source text word alignment set and a candidate word alignment set are put into the prompt template, and a highly-aligned external word alignment set is used for large model context learning to generate an optimal translation result; according to the method, various translation errors of a large model in a low-resource environment are relieved.

The invention discloses a reinforcement learning coach-driven context learning general motion control method and system, and the method comprises the steps: receiving the structure parameters of a plurality of predefined robots, carrying out the random sampling in a preset parameter space, and generating a plurality of robots of different structures; target tasks are defined, and reinforcement learning coaches corresponding to the target tasks are trained based on robots of different structures; in the simulation environment, guiding the robot to execute tasks by using a reinforcement learning coach, and recording a task execution track corresponding to each target task; extracting a context sequence with a fixed length from the task execution trajectory by using a general context learning framework, and performing cross-task meta-training by taking maximization of expected accumulated rewards under each target task as a training target so as to optimize context learning model parameters and obtain a pre-trained robot control model; a pre-trained robot control model; and obtaining a target control action which is output by the pre-trained robot control model and corresponds to the current state.

The invention provides a knowledge editing method based on multilingual semantic retrieval, and belongs to the technical field of natural language process.The method achieves cross-lingual knowledge updating through the two stages of multilingual knowledge retrieval and context editing and comprises the steps that firstly, a multilingual retrieval model based on XLM-R is used for mapping queries and knowledge base entries to a shared semantic space, and then the shared semantic space is used for conducting context editing; semantic correlation is judged through a classifier, and target language knowledge is retrieved; and splicing the retrieval result and the query in a zero sample or small sample mode to generate a prompt template, and inputting the prompt template into a large language model to complete editing. The method is characterized in that monolingual limitation is broken through, collaborative updating of 12 languages is supported, a retrieval-editing decoupling architecture is adopted to achieve model irrelevant adaptation, and retrieval and context learning are fused to improve editing accuracy. The method can efficiently correct multi-language large model fact errors, is suitable for scenes such as search engines and intelligent customer services, avoids the cost of full-model retraining, and remarkably improves the efficiency and accuracy of cross-language knowledge updating.

Provided in the embodiments of the present description are a code error analysis method and apparatus, and a log scanning rule generation method and apparatus. The log scanning rule generation method comprises: acquiring a program code to be analyzed which corresponds to a code error analysis task; determining an analysis task data group on the basis of said program code, wherein the analysis task data group comprises said program code, and a first error code sample and a corresponding log scanning rule sample; and inputting the analysis task data group into an error analysis model, such that the error analysis model generates a log scanning rule, wherein the error analysis model is used for performing context learning on the basis of the first error code sample and the corresponding log scanning rule sample, and generating the corresponding log scanning rule for said program code, and the log scanning rule is used for performing error scanning on a program log. Therefore, the efficiency of error analysis for a program code is improved, and the development efficiency is improved.

The invention discloses a sheet metal part manufacturability reasoning method based on space-semantic map alignment, and relates to the field of manufacturing-oriented design evaluation and industrial knowledge reasoning, and the method comprises the following steps: carrying out geometric analysis on a CAD geometric model of a sheet metal part to be evaluated; abstracting the geometric features and the topological / metric spatial relationship thereof into a computable spatial semantic graph; performing semantic analysis on the process specification described by a natural language, converting the process rule into formalized logic check expression by using a large language model through context learning, and generating an executable domain-specific language check script; executing the script on a spatial semantic graph, realizing deterministic reasoning through graph matching and attribute verification, and completing accurate mapping and violation detection of text rules and geometric features; and outputting an interpretable diagnosis result containing violation feature positioning, triggering rules and numerical evidence. In order to solve the problems that a process rule'natural language-geometric model 'has a semantic gap, a traditional rule system is poor in adaptability, and an end-to-end learning method is high in data dependence and cannot be explained, a new rule can be quickly adapted under the condition that a large amount of data does not need to be labeled and a model does not need to be retrained; the method can accurately identify the violation of the micro-size and spatial relationship, and has reasoning preciseness, interpretability and engineering availability.

The application discloses a large language model prompt word injection attack detection method and device based on context learning, and belongs to the technical field of artificial intelligence, and comprises the following steps: based on a Bert pre-training model, inherent features and dependency relationships between different levels and different labels are learned by introducing a label-based attention module, a multi-level, multi-label and fine-grained classification model for prompt word injection attack is designed, and accurate identification of the prompt word injection attack is realized.Meanwhile, according to the context learning, the prediction ability of the classification model is combined with the ability of the large language model, and the defense ability of the large language model to the prompt word injection attack is improved.The application can automatically detect the prompt word injection attack, improve the effectiveness and comprehensiveness of detection, and can be effectively applied to the field of large model security detection.

The invention discloses a cross-organization emergency response process model extraction method based on a large language model, belongs to the field of process mining and natural language processing, and constructs a large language model general prompt framework containing emergency response process element definition and judgment criteria. Based on a general prompt framework, a prompt strategy fusing role playing and context learning is adopted to extract explicit entities such as emergency organizations and response tasks and relationships of the explicit entities. Inferring and extracting implicit entities and implicit relation elements on the basis of explicit elements by adopting a thinking chain fused prompt strategy; and finally, making a mapping rule to convert the extracted complete process elements into a logically coherent cross-organization emergency response process model, and presenting a result in a graphical manner. According to the method, domain data training is not needed, accurate flow element extraction can be achieved under the condition of few samples, the problem of implicit flow element missing is solved, and technical support is provided for emergency decision making.

The invention discloses a method and a system for automatically repairing context learning code vulnerabilities of a large language model, and aims to solve the problems that invalid patches are easily generated, vulnerability causes are misunderstood and verification is lacked in existing LLM zero sample repairing, and a traditional method depends on annotated data or test input. The method comprises the following steps: processing a vulnerability code-patch pair containing a plurality of different CWE types and a target vulnerability code, extracting vulnerability-related codes and constructing an example pool; an adaptive example is selected through comprehensive comparison of semantics, lexical and structural similarity; generating prompt words in combination with related codes and adaptive examples, driving LLMs to generate candidate patches, and outputting effective patches after verification of multiple LLMs; the system comprises a code processing module, an example selection module and a patch generation and verification module. According to the method, LLM and a large amount of annotated data do not need to be finely adjusted, the repair efficiency and accuracy are improved, the labor cost is reduced, multi-language extension is supported, and industrialization potential is achieved.

The invention relates to the technical field of urban rail transit and artificial intelligence, and discloses a subway operation accident knowledge graph construction method and system, a terminal and a storage medium. According to the method, firstly, an ontology knowledge base defining entities, relationships and attribute specifications is constructed on the basis of subway operation accident scheduling logs, and an example pool containing original logs and standard triple mapping is constructed according to the ontology knowledge base; secondly, carrying out beforehand self-checking in a knowledge extraction stage: utilizing a context learning mechanism to guide a large language model to carry out preliminary extraction by retrieving similar examples in an example pool; and decomposing an extraction task into multiple steps such as entity recognition and relationship judgment by utilizing a thinking chain mechanism, and generating a knowledge graph to be verified through gradual reasoning. And finally, post-event self-verification is carried out based on the ontology library and the large language model, multi-dimensional verification is carried out on the map, a final subway operation accident knowledge map is generated, and the accuracy and reliability of the map in the professional field are ensured.

The invention provides a large-model intelligent label synthesis and data automatic labeling integration method and system, and belongs to the technical field of label synthesis and data labeling, and the method comprises the steps: carrying out the semantic embedding and robust clustering of text data, and obtaining a stable cluster set; when new data is introduced, semantic consistency alignment of cross-round clustering results is realized through a confusion matrix matching strategy, and label drift is inhibited; maintaining an editable hierarchical label directed acyclic graph to support label system evolution; driving a large language model to generate a high-quality and interpretable cluster-level semantic tag based on the representative sample; carrying out automatic annotation and confidence evaluation by using large model context learning for clustering non-attribution or low-confidence samples; and propagating the cluster-level labels to the instances, and combining the cluster-level labels with an automatic labeling result to construct a full-process traceable label management mechanism. According to the method, the efficiency, quality and consistency of text labeling are improved, and powerful support is provided for large model training and intelligent data management.

The invention discloses a multi-modal deep forgery detection method and system based on multi-agent collaborative reasoning and context learning, and the method comprises the steps: carrying out the analysis of an image in an input image-text pair through a visual expert module, and generating a visual description corresponding to the content of the image; performing multiple rounds of interactive questions and answers on a text in the input image-text pair based on the visual description by utilizing a multi-agent module, and generating a dialogue history containing multiple rounds of questions and answers; the method comprises the following steps: searching Top-k examples similar to the semantics of a current image-text pair from a pre-constructed multi-modal knowledge base through a search module, and constructing a context example set; and by summarizing the expert module, fusing the dialogue history, the text and visual description of the current image-text pair and the context example set, outputting authenticity judgment and forgery type prediction of the to-be-detected image-text pair. According to the embodiment of the invention, the accuracy, generalization ability and result interpretability of multi-modal depth forgery detection can be improved.

The invention discloses a method for generating an intelligent agent recognizable structured language, and belongs to the technical field of intelligent agent control. According to the method, a natural language task requirement input by a current user and external environment state parameters collected by an external environment detection module and related to the natural language task requirement are input into a large model; and finally, obtaining a structured text for intelligent agent equipment corresponding to the natural language task demand to read and execute a corresponding user demand task. According to the generation method of the intelligent agent recognizable structured language, cue word module splicing and context learning are fully utilized, a mode of combining a language large model and event extraction is adopted, an atomic event sequence is generated and automatically modified, intelligent task planning is effectively achieved, and the planning accuracy is improved.

The invention provides a generative knowledge object extraction method and system based on context learning, and relates to the technical field of generative artificial intelligence, and the method comprises the steps: carrying out the coding preprocessing of a target text through a first Transform encoder in a double-tower structure, and obtaining a target vector; traversing and matching the target vector in a pre-stored vector library to obtain a target matching vector which corresponds to the target example; forming a target structured input prompt in combination with the target text and the target example; and under the constraint of the target structured input prompt, performing extraction analysis on the target text by the generative knowledge object extraction model to obtain a target extraction result. According to the method and the device, the technical problem of relatively poor entity recognition result accuracy of a generative entity recognition model in the prior art can be solved, and the technical effect of improving the accuracy of the generative named entity recognition model is achieved.

The application provides a method and system for alarm research and judgment and empowerment optimization based on reinforcement adaptation, which comprises constructing an intelligent research and judgment model, modeling the environment of the intelligent research and judgment model, constructing a network attack path according to the intelligent research and judgment model, and identifying a network attack by using a known signature of the network attack or a rule-based detection technology; classifying and labeling data of a network security device by using an SVM classification algorithm and training the intelligent research and judgment model, establishing a threat identification strategy of the network attack, and identifying features of the network attack; performing context learning and state prediction by using the intelligent research and judgment model, and optimizing the threat identification strategy of the network attack; generating adversarial samples by using known threats of the network attack through slight perturbation of data fields of normal HTTP requests, and performing adversarial training in combination with fake HTTP requests to identify deceptive network attacks. The application realizes cross verification and correlation analysis of high-quality and multi-source threat intelligence, and improves attack defense capability.

The application discloses a kind of based on context learning's incremental user portrait method and system, belong to electric power system user portrait technical field, the method is: based on the small sample load data of to-be-tested incremental user construction incremental user portrait task pair;Obtain several current context task pairs, and incremental user portrait task pair and several current context task pairs are combined to form test sample;Based on test sample and pre-trained user portrait model, obtain incremental user portrait result;Pre-training process includes: obtaining the stock historical load data sequence set of several stock users;Based on stock historical load data sequence set, preset historical window length and preset future window length, construct the preset portrait model training data pool corresponding to several stock users;The pre-constructed basic portrait model is trained based on preset portrait model training data pool, and user portrait model is obtained, therefore, by implementing the application, small sample, high-precision portrait to incremental user can be realized.

The application discloses a method and device for efficient automatic power consumption optimization of chip physical design, solves the problem that the prior art cannot balance the time cost and optimization effect in design space exploration, realizes efficient exploration of high-dimensional continuous physical design parameters, and thus optimizes design parameters and reduces chip power consumption under the premise of guaranteeing design constraints; the method comprises the following steps: determining physical design parameters and boundaries based on a chip process node, sampling and constructing an initial data set; using a pre-trained large model to establish a small sample power consumption evaluation proxy model through context learning; parameter generation model iterative optimization: generating a candidate parameter combination, inputting the proxy model to obtain predicted power consumption, and updating model parameters until convergence according to the predicted power consumption; after training, the model generates a parameter combination to call an EDA tool to obtain real power consumption, so as to feed back the optimization model, and finally output a chip design with optimized power consumption.

This application relates to the field of cross-task shared optimization technology for code intelligence, and particularly to a method, apparatus, and electronic device for cross-task shared optimization of code intelligence. The method includes: acquiring a hierarchical context learning task and a code-natural language multimodal contrastive learning task; generating training data based on the hierarchical context learning task and the code-natural language multimodal contrastive learning task; training a pre-set pre-trained language model based on the training data; freezing the network parameters of the trained pre-trained language model; transferring the shared parameters of the pre-trained language model to the downstream code target task for parameter optimization; inputting the code to be tested into the optimized pre-trained language model; and outputting the optimization result of the code to be tested through the pre-trained language model. This solves the problems of weak cross-task transfer and generalization ability of pre-trained models and low parameter optimization efficiency in related technologies.

The invention discloses a parameter optimization method based on a large language model and a depth deterministic strategy gradient, which is used for solving the problem of information age deterioration caused by data packet conflict and vehicle speed related channel uncertainty in semi-persistent scheduling of the Internet of Vehicles. Firstly, an AoI calculation model influenced by the vehicle speed, the vehicle density and the resource reservation interval is established, then a double-path optimization scheme is designed, DDPG is stimulated through a state space and a reward function, and optimal parameter configuration is generated by utilizing LLM through situational learning; through the above method, the LLM can significantly reduce AoI after a small number of examples are accumulated, model training is avoided, and the DDPG method has more stable performance after training.