280 results about "Test question" patented technology

The invention discloses a middle and primary school test question intelligent generation method based on an education big model. The method comprises the steps of generating a structured education data set; constructing an education large model specially used for intelligent generation of middle and primary school test questions; inputting target knowledge points, question types, difficulty and grade parameters, and generating a candidate test question set; setting a test question fitness function, comprehensively evaluating the knowledge coverage rate, difficulty distribution and language expression indexes of the candidate test questions, screening and optimizing the candidate test question set, and generating an optimized candidate test question set; detecting semantic accuracy, logic preciseness and teaching conformity of the test questions by using an automatic quality evaluation mechanism, and outputting test question evaluation results; and continuous optimization and self-adaptive updating of test question generation are realized. According to the method, the optimal multi-question type test question set can be automatically output on the premise of ensuring comprehensive coverage of knowledge points, reasonable difficulty distribution and excellent language quality, and the quality, balance and diversity of automatically generated test questions are greatly 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 multi-agent proposition method and system based on collaborative reinforcement learning. The method comprises the following steps: constructing a multi-agent proposition framework comprising a test question generation agent and a quality inspection agent based on a large language model; constructing training data on the basis of a target subject test question set, respectively supervising and adjusting large language models on which a test question generation agent and a quality inspection agent are based, and training a reward model for evaluating the output quality of each agent for the agent; and performing multi-agent collaborative reinforcement learning training on the test question generation agent and the quality inspection agent by using the reward model to obtain a trained agent model weight. And loading the trained agent model weight into a multi-agent collaborative proposition framework, and receiving proposition requirements to generate final test questions and reference answers. A multi-agent cooperative reinforcement learning mechanism is introduced into a multi-agent system, so that the agents can form a stable and efficient cooperation strategy in a real proposition task interaction environment.

The invention discloses a multi-modal large language model training method and system, and relates to the technical field of multi-modal large model data processing, and the method comprises the following steps: training a first large model through querying a question set, a positive sample and a hard negative sample; inputting the plurality of test samples into the trained first large model, and generating a plurality of second answers based on a first preset prompt; sorting each test sample based on the plurality of second answers, and retrieving to obtain a previous candidate multi-modal document related to the test question; and training the second large model through the test problem and the corresponding previous multi-modal document. According to the method, the positive samples and the hard negative samples are jointly used for training, the model is forced to capture the fine-grained semantic boundary of correlation judgment through a contrast learning mechanism, the distinguishing capacity of the model for difficult samples is remarkably improved, and the mistaken arrangement phenomenon is avoided.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent proposition method, system and device for simulating expert propositions and a storage medium, and the method comprises the steps: constructing a structured knowledge network fusing knowledge points, cognitive levels and proposition specifications; establishing a material vector database associated with the knowledge point system; according to the target knowledge point, proposition constraint information is extracted from the knowledge network, related materials are retrieved from a vector library, and the related materials are combined into a generative prompt to be input into a large language model to generate a test question first draft; inputting the test question first draft into at least one large language model serving as a simulation answering agent, evaluating the quality of the test questions by analyzing the answering process and result, and outputting the test questions reaching the standard. According to the method, the proposition efficiency and consistency can be remarkably improved, absolute dependence on expert experience is reduced, and automatic output of high-quality test questions is achieved.

The invention discloses a personalized education examination management method based on artificial intelligence, relates to the technical field of education examination management, and introduces and fuses teaching program data and teaching calendar data, dynamically calculates teaching progress offset, accurately constructs a time sequence embedding space reflecting an actual teaching progress, and provides a personalized education examination management method based on artificial intelligence. The problem of proposition deviation caused by different teaching progresses of areas, schools or classes is fundamentally solved; the system can automatically sense and adapt to teaching plan changes such as course adjustment and course halt, and the generated test questions are strictly limited in a knowledge point range taught at the current stage, so that the examination evaluation result can truly and effectively reflect the mastering condition of students on the currently learned content, and the reliability and validity of the examination are greatly improved; the dynamically constructed teaching time sequence embedding space is used as a core constraint condition and is deeply integrated into a prompt project and decoding generation process of a large language model, so that accurate and intelligent control on test question generation content is realized.

The invention belongs to the technical field of education artificial intelligence and deep learning model optimization, and discloses a thinking tree enhancement-based test question knowledge system intelligent labeling method and system.The thinking tree enhancement-based test question knowledge system intelligent labeling method comprises the steps of collecting question data by introducing a thinking chain enhanced data generation mechanism, and generating a high-quality training sample; and a control mechanism is set, a question generation process is optimized, and the generated question is ensured to accord with teaching specifications in the aspects of knowledge point coverage, grade adaptability, difficulty matching and the like. Through a text encoder based on comparative learning optimization, the model can accurately carry out semantic alignment on questions and knowledge point labels, and label path information is fused through a double-coding mechanism, so that the hierarchical relationship between the labels and the capture capability of semantic dependence are improved. Finally, the constructed multi-label prediction model significantly improves the accuracy and generalization ability of knowledge point labeling, and can provide accurate support for personalized learning recommendation and teaching resource allocation.

The embodiment of the invention discloses a test question output method and device based on multi-model collaboration, electronic equipment and a medium. A specific embodiment of the method comprises the steps of performing multi-source data fusion on a multi-source test question association data set, and then performing knowledge point association analysis to obtain a dynamic test question knowledge point graph; performing multi-hop graph reasoning query on the dynamic test question knowledge point graph to obtain a test question associated knowledge point set; performing model collaborative generation on the test question generation request information and the test question associated knowledge point set to generate an initial test question set; performing quality evaluation on the initial test question set to obtain a test question quality evaluation value set; dynamically adjusting the initial test question set to obtain an adjusted test question set and storing the adjusted test question set; and carrying out expansion adaptive test paper group rendering display on the adjusted test question set to obtain a test question test paper and printing the test question test paper. According to the embodiment, the test question quality can be improved, the test question generation efficiency is improved, the test question generation duration and display duration are shortened, the test question display quality is improved, and waste of display resources is reduced.

The invention discloses a big language model capability evaluation method and a related device, and relates to the technical field of model evaluation, and the big language model capability evaluation method comprises the steps: obtaining an answer generated by each big language model in a to-be-evaluated big language model set for each test question in a test data set; generating a corresponding answer quality evaluation rule for each test question by using the rule generation large model; combining every two large language models, and combining two answers generated by the same model pair for each test question to obtain an answer pair corresponding to each model pair on each test question; for each test question, judging the advantages and disadvantages of the two answers contained in each answer pair according to the judgment rule corresponding to the test question; and determining an ability evaluation result of each large language model according to an evaluation result of each answer pair. According to the evaluation method disclosed by the invention, the capability of a plurality of large language models can be automatically evaluated, the evaluation efficiency is relatively high, and the evaluation result is objective and reliable.

The invention discloses a large model automatic evaluation method, apparatus and device, and a readable storage medium. The method comprises the steps of receiving an evaluation request for a target large model; based on the type of the evaluation request, determining a question and answer record of the target large model; wherein when the type of the evaluation request is model ability evaluation, the question and answer record comprises a plurality of test question and answer cases, and each test question and answer case comprises a test question, an expected answer of the test question and a final answer output by the target large model to the test question; when the type of the evaluation request is a model output decision, the question and answer record comprises a real-time question and answer case, and the real-time question and answer case comprises a question input by a user in real time and a plurality of candidate answers output by a target large model to the question in real time; determining an evaluation index set of the target large model based on the question and answer record; and based on the type of the evaluation request, the question and answer record and the evaluation index set, evaluating the target large model through a preset judgment large model, and obtaining an evaluation result.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The invention provides an automatic test question generation method and device for the urban rail industry and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining question setting information input by a user, wherein the question setting information comprises knowledge point information, question setting parameters and question setting rules; obtaining a target knowledge fragment from a knowledge base according to the knowledge point information and the question setting parameters; according to the target knowledge fragment, the question setting parameters and the question setting rules, generating at least one question by adopting a question generation model; evaluating each question in at least one evaluation dimension by adopting a plurality of question evaluation models to obtain a plurality of scores and modification suggestions of each question in at least one evaluation dimension; according to the multiple scores of each question in the at least one evaluation dimension, calculating a total score and a confidence coefficient of each question; and optimizing each question by adopting a question generation model according to the total score, the confidence coefficient and the modification suggestion of each question. The test questions can be automatically generated, and the quality of the test questions is ensured.

The invention discloses a self-adaptive test question generation method and device, a medium and equipment, and belongs to the field of test question generation. Multi-source learning data of a user is obtained and input into a user intention recognition model, and the learning intention category of the user is accurately determined. And according to the category, screening a matching template from a structured Prompt template library, and filling a slot in combination with user portrait data to generate a natural language Prompt. Then, knowledge fragments in the relay protection field knowledge base are retrieved and spliced through an RAG knowledge enhancement mechanism, an input sequence is formed, finally, a large language model is input to generate personalized relay protection field test questions, and the learning efficiency and experience are improved. The problem that the relay protection test questions cannot be accurately and efficiently generated in the prior art is effectively solved.

The invention relates to the technical field of data generation, and discloses a training content generation method and system based on a deep learning model and a storage medium, and the method comprises the steps: obtaining basic content data, and extracting text, table and format content to generate structured data; identifying a content structure and extracting training knowledge elements through deep learning model semantic analysis, and constructing a knowledge graph in combination with an association relationship between the elements; calculating the occurrence frequency, the content chapter and the core degree of the training knowledge elements to obtain a difficulty coefficient; obtaining training parameters, calculating a knowledge coverage coefficient and a knowledge depth coefficient, and generating initial content in combination with the first text framework and the large language model; and performing full-content verification and correction through the second text framework to obtain training content. According to the method, the training content is automatically generated, the problems of low efficiency, high cost, unstable quality and the like of manual question setting are solved, and the scientificity and pertinence of the training content are improved.

The disclosure relates to digital assistant evaluation. In an example method, in response to an evaluation request for a target digital assistant, at least one set of test cases for the target digital assistant is obtained, and each set of test cases includes at least one test question related to a chat skill of the target digital assistant. The at least one set of test cases is provided to the target digital assistant to obtain a reply to the at least one set of test cases by the target digital assistant. A target evaluation index for the target digital assistant is determined based at least on the at least one set of test cases and the reply to the at least one set of test cases by the target digital assistant. A quality evaluation result of the target digital assistant is determined based on the target evaluation index.

A system is provided.SOLUTION: A system comprising: means for receiving and pre-processing lesson content; means for operating a generative AI model for automatically generating lesson plans and test questions based on the pre-processed content; means for transmitting the generated lesson plans and test questions to terminals of educational institutions; means for collecting lesson videos through cameras installed in classrooms; means for automatically generating objective evaluations of students; and means for transmitting the generated evaluation results to the terminals of the educational institutions.SELECTED DRAWING: Figure 1

The invention discloses an electric power safety knowledge testing method based on a large language model and enhanced retrieval generation. The method mainly comprises the steps of constructing a vector knowledge base, constructing a dynamic interaction generation workflow framework driven by a multi-role large language model, performing safety knowledge testing and quantifying the electric power operation safety awareness of a user. According to the invention, the problem with high scene adaptation degree can be dynamically generated in combination with the scene of the operation work order and the user interaction result, so that the test problem accuracy is improved; the quality of test content is improved through cooperative work of a multi-role large language model and in combination with an enhanced retrieval generation module; a dynamic adjustment strategy is implemented according to a user feedback mechanism, so that knowledge blind spots of the user can be effectively identified; the safety awareness of the user is quantified, targeted consolidation is carried out, and the electric power safety knowledge level of the user is improved.

The invention discloses a dynamically configured accounting document question setting and test paper composition method and system, relates to the field of accounting education, and provides the following scheme: performing feature extraction and comparison on question solving information and solution sets based on a machine learning technology to generate used and unused solution sets; screening the effective solutions which are not fully used by using a threshold value, and counting the occurrence times of the effective solutions in the historical solution set as effective rates for sorting; learning grades are divided according to historical scores of students, and targeted questions are generated by differentially retaining an efficient solution; and constructing a test question template based on the question category, and performing dynamic test paper composition by utilizing a machine learning optimization algorithm. Deep mining of hidden knowledge points is realized through intelligent analysis of a solution using mode; through accurate assessment of student ability and question adaptation, the problems of simplification and staticization of a traditional test paper composition method are solved, and the personalized level and training effect of accounting document teaching are improved.

One example method for evaluating a quality of chatbot answers after changes have been made to internal chatbot pre-processing tasks and / or post-processing tasks, includes filtering, based on information identifying changes that have been made to a reference version of a chatbot, a set of representative questions that have been posed by one or more users to the reference version of the chatbot, to obtain a test set of test questions related to the changes, obtaining test answers, generated by a new version of the chatbot, to the test questions, scoring the test answers, and performing an automated testing process that includes comparing scores of the test answers with scores of answers provided by the reference version of the chatbot to the test questions.

The embodiment of the invention provides a multi-layer Agent and expert cooperation test question scoring system and method. The system comprises an answer receiving module, an answer content analysis module, an objective question scoring module, a multi-agent cooperation scoring module, an expert verification module and a data analysis module. Wherein the multi-agent collaborative scoring module performs multi-dimensional scoring through a multi-level Agent and generates a result through weighted consensus; the expert verification module supports an expert to check and determine a result; and the data analysis module generates an optimization suggestion based on the verification result and feeds back the optimization suggestion to the multi-level Agent. According to the system, a three-level cooperation mechanism of answer detail analysis-AI driven scoring-professional field personnel verification is constructed, a multi-Agent scoring and expert auditing feedback mode is innovatively adopted, Agents are promoted to carry out self iterative evolution, intelligent and precise scoring of various test questions is achieved, and meanwhile continuous optimization of the system is supported to adapt to teaching requirements.

The invention provides a test question similarity rapid detection method and system based on content understanding. The test question similarity rapid detection method comprises the steps of performing semantic cleaning and content standardization on test questions to be processed, and obtaining test question data after content standardization; performing structured attribute filtering on the test question data after content standardization to obtain filtered test question data; index similarity comparison and examination point similarity comparison are carried out on all the test questions corresponding to the filtered test question data, and index similarity numerical values and examination point similarity numerical values of all the test questions are obtained; according to the index similarity values and the examination point similarity values among all the test questions, determining comprehensive similarity values among all the test questions; and extracting and displaying each pair of test questions with the comprehensive similarity value exceeding a preset similarity value. The system comprises modules corresponding to the steps of the method.

The application discloses a kind of based on big language model and ability atlas's wisdom pastor authentication examination method and system, belong to artificial intelligence technical field.The application first constructs the ability atlas based on knowledge, skill, behavior three-dimensional ontology model;Obtain examinee multimodal data and generate initial ability vector;Through double-path dynamic gate graph neural network, real-time update ability vector, the network includes erase gate and increase gate and is updated by graph convolution layer propagation;According to ability short board, determine aggressive factor and combine incidental factor, use big language model to dynamically generate the next logical unique test question;Repeat interaction until the end of examination;Finally, according to all updated ability vector, generate digital ability portrait report.The application realizes dynamic individualized surveying and mapping, anti-cheating and deep characterization of talent ability structure, improves the precision and security of wisdom pastor authentication examination.

1. Name of the product in this design: Dynamic graphical user interface for test question options in electronic devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The graphical user interface in this design is used to display dynamic information about the test question options. 7. Description of the changing states of the graphical user interface: The interface shown in the main views of Design 1-Design 4 is the dynamic information display interface for the test question options. Specifically, in the interface shown in the main views of Design 1-Design 4, the lower part of the interface displays answer buttons for "YES" and "NO". By clicking one of the answer buttons, the interface can switch sequentially from the main view to the interfaces shown in Figure 1 and Figure 2.

The invention relates to the technical field of intelligent education, in particular to an intelligent practical operation assessment system and method based on multi-modal perception, and the system comprises a question setting and rule configuration module, a local assessment terminal, a hardware connection monitoring module and a remote management service terminal. The question setting and rule configuration module is used for generating a structured test question rule file, the hardware connection monitoring module is used for collecting conduction relation data, and the local assessment terminal comprises a man-machine interaction layer, a sensing and monitoring layer and an application logic and control layer; the remote management service terminal is used for data storage and instruction issuing. The system has the following advantages and effects: through the cooperation of the question setting and rule configuration module, the local assessment terminal, the hardware connection monitoring module and the remote management service terminal, the operation behavior and the tool state are identified; multi-mode perception, automatic monitoring and accurate scoring of electrician practical training type practical operation assessment are realized, and the objectivity, efficiency and traceability of assessment are improved.

The invention discloses a subjective question intelligent marking system and method based on a large language model, and relates to the technical field of online education and examination intelligent marking, and the system comprises a scoring rule configuration module, an AI scoring engine module, a teacher review interaction module and a result presentation and archiving module. The scoring rule configuration module is used for receiving AI scoring related parameters configured by a user; the AI scoring engine module integrates a large language model and is used for receiving test question related information, examinee answers and AI scoring related parameters and carrying out natural language understanding, knowledge reasoning, logic analysis and standard comparison on the basis of the large language model to obtain an AI scoring result; the teacher re-checking interaction module provides an interaction interface for the user to check the AI scoring result to obtain a final scoring result; and the result presenting and archiving module is used for archiving, storing and visually displaying the final scoring result. According to the invention, the manual reviewing cost can be reduced, and the accuracy and efficiency of subjective question reviewing are improved.

The invention relates to the field of question search, and discloses a multi-modal question search method and system based on deep learning, and the method comprises the steps: firstly obtaining question bank test question multi-modal data, generating a fusion semantic vector through joint coding, and constructing a vector index database; the method comprises the following steps: acquiring multi-modal data of test questions to be searched for preprocessing, extracting features to generate query semantic vectors, inputting the query semantic vectors into an index database for retrieval, and obtaining candidate similar test questions according to semantic similarity; then semantic association analysis is carried out, the multi-modal data of the test questions to be searched and the multi-modal data of each candidate test question are finely compared, the association degrees of the test questions to be searched and the candidate test questions in the semantic level are mined, and a semantic matching degree score is obtained; and finally, reordering the candidate similar test questions according to the scores, and accurately outputting the target test question with the highest similarity to ensure that a retrieval result which most meets the requirements is provided for a user, so that efficient and accurate question search is realized, the retrieval accuracy and the result reliability are greatly improved, and the adaptability of the system to new question types and complex question types is enhanced.

The invention relates to the technical field of information processing, in particular to a person and post matching method, device and equipment based on skill verification and a storage medium. An initial user portrait is constructed according to resume information, and the skill field and skill level are determined based on self-described core skills and self-described skill proficiency; and dynamically generating a test question set matched with the skill field and the skill level, collecting answer results after the user completes the test question set, introducing skill verification to improve authenticity, calculating skill level parameters of each skill of the user by using a preset project reaction theoretical model, and calculating the skill level parameters of each skill of the user. The initial user portrait is dynamically corrected according to the skill level parameters, so that the skill mastering degree can be hierarchically defined and dynamically adjusted, the potential promotion space and development potential of the user can be found, and the final user portrait is matched with the post knowledge graph to improve the user experience. And a post recommendation list is generated according to the matching result, and a skill gap analysis report is output, so that the post matching accuracy is improved.

The invention provides an AI-based test question generation method and system, and relates to the technical field of test question generation. The method comprises the following steps: collecting proposition record data and extracting a plurality of proposition failure samples, determining a failure attribute set of each proposition failure sample and extracting a plurality of attribute conflict sets, carrying out minimum conflict analysis on the plurality of attribute conflict sets to construct a proposition parameter constraint rule, extracting a modification operation path of each proposition failure sample, and establishing a proposition parameter constraint rule; performing partial order relation identification on the plurality of question attributes, constructing a joint shell domain of the plurality of question attributes, generating a candidate generation space of each question attribute, and constructing a plurality of diverse proposition combinations; and obtaining a test question to be output, performing parameter conflict detection based on the proposition parameter constraint rule, identifying a plurality of conflict attributes of the test question to be output, and optimizing the test question to be output in combination with the conflict attributes and the plurality of diversity proposition combinations to generate a target test question. The efficiency and the quality of test question generation are improved.

The application discloses an online education data intelligent planning system and method based on big data analysis, and belongs to the technical field of education data analysis. The system comprises a learning text management module, a data acquisition module, an analysis database, a model management module and a test question generation module; the learning text management module is used for collecting different learning texts for user learning and calculating the complexity of each learning text; the data acquisition module is used for respectively acquiring reading behavior data and test question data of different user learning texts; the analysis database is used for storing historical data; the model management module is used for analyzing the influence of different reading behaviors on the understanding degree of user learning texts; and the test question generation module is used for automatically generating a test question set about the user and pushing the test question set to the user; the application automatically generates a test question set about the user by connecting the weak knowledge points of the user, and improves the intelligent service level of the online education platform.

The invention relates to the technical field of English learning cognition evaluation, and discloses an English learning cognition state dynamic evaluation method based on multi-mode micro-behavior time sequence fusion, which comprises the following steps of: constructing a similar matrix according to the similarity among English knowledge points in English knowledge cognition evaluation test questions, and constructing an English learning cognition state dynamic evaluation model on the basis of the similar matrix; predicting a target expectation of the testee when answering the English knowledge cognition evaluation test question; constructing a thinking style set to describe a learning strategy of the testee, and simulating a speed and accuracy tradeoff relation when the testee answers the English knowledge cognition evaluation test question according to the learning strategy and a dynamic logic technology; and constructing a cognitive state dynamic evaluation model on the basis of the tradeoff relationship between the speed and the accuracy, calculating the knowledge mastering level of the testee, and comparing the knowledge mastering level with the target expectation to obtain the English learning cognitive state of the testee. According to the invention, through fusion and dynamic adjustment of the multi-modal data, personalized and accurate learning evaluation can be provided for each tested person.