707 results about "Test question" patented technology

The invention discloses a personalized dynamic question setting method and system based on a large language model. The method comprises the following steps: S1, collecting multi-source data such as historical answer records, subject scores and learning directions of students, and constructing structured feature vectors; s2, evaluating the mastery degree of the students on the complete knowledge system through the pre-training model, and calculating the knowledge point coverage; s3, a default test question set is extracted from the question bank, knowledge point mastering scores of the students are converted into weights through an attention mechanism, question difficulty distribution is dynamically adjusted, and weak link training is strengthened; and S4, fusing the student characteristics, the grasp score, the coverage degree and the adjusted difficulty distribution, and generating and pushing test questions through a personalized question setting model finely adjusted by a large language model. According to the method, the powerful semantic comprehension and generation capability of the large language model is utilized, the learning track of students can be analyzed, the question difficulty, question type distribution and the knowledge point coverage range are dynamically adjusted, and therefore precise and personalized question recommendation is achieved.

The invention discloses an intelligent auxiliary teacher lesson preparation system and method based on an education large model. The method comprises the following steps: S1, generating a standardized teacher lesson preparation data set; s2, constructing a teaching knowledge graph; s3, generating preliminary intelligent lesson preparation content; s4, presenting the preliminarily generated intelligent lesson preparation content, and providing an interactive interface combining a natural language and voice recognition; s5, generating the adjusted intelligent lesson preparation content; and S6, according to historical lesson preparation data of the teacher and the interactive feedback data, performing adaptive learning and optimization on the intelligent lesson preparation content, adjusting weights of key knowledge points in the teaching knowledge graph, updating a content generation strategy of the large education model, and generating a final intelligent lesson preparation scheme. According to the method, the response capability of the intelligently generated content to the teaching intention of a teacher is remarkably improved, the teaching content and the test question style are supported to dynamically cooperate with classroom feedback along with the teaching rhythm, and an intelligent decision support basis is provided for large-scale personalized teaching.

The invention discloses a junior high-school English reading understanding test question intelligent analysis method based on a large model, which comprises the following steps: S1, obtaining answer texts of junior high-school English reading understanding test questions completed by students and corresponding test question texts, and answers are from published standard answers; s2, preprocessing the input test question text and answer text; s3, performing semantic level deep analysis on the test question text by utilizing a deep context understanding large model; s4, according to the test question text and the standard answer, in combination with understanding of the logical reasoning large model on text context and logical relationship, analyzing the semantic matching degree of the student answer, and based on the standard answer and the logical reasoning large model, generating a semantic and logic comprehensive evaluation result of the student answer; s5, dynamically generating learning suggestions based on the semantic and logic comprehensive evaluation result of the student answers; and S6, outputting a test question analysis report. According to the invention, the answers of the students are analyzed more accurately, and targeted learning guidance is provided, so that the teaching effect and the learning experience of the students are remarkably improved.

Disclosed in the present application are an evaluation method and apparatus for a large language model, a storage medium and a computer device. The method comprises: on the basis of a target application field of a target large language model to be evaluated, acquiring a test question bank corresponding to the target large language model, wherein the test question bank comprises objective questions belonging to the target application field, and question stems and answers of the objective questions are obtained by analyzing corpus samples of the target application field; and using the test question bank to test the target large language model, so as to obtain an output result of the target large language model for the tested objective questions; and, on the basis of answers corresponding to the tested objective questions and the output result of the target large language model for the tested objective questions, evaluating the target large language model.

The invention relates to a zero-sample-based multi-agent integrated test question generation method and system, and relates to the technical field of intelligent education. Learning feedback data of a user is collected in real time through a data collection module and is stored, cleaned and preprocessed; according to the learning feedback data, test questions are generated through multi-agent cooperation, all generation models in the multi-agent are in one-to-one correspondence with test question types, and test question difficulty and a knowledge point coverage range are dynamically adjusted; the generated test questions are detected through a dynamic evaluation module, the detection content comprises the correct rate of answers corresponding to the test questions, the deviation between personal feedback data and a preset threshold value and logical errors in wrong answers, and a detection result is fed back to the generation model; and if the result is unqualified, the generation model adjusts parameters or generates logic according to feedback, and otherwise, the test questions which are verified to be qualified are pushed to the user. Compared with the prior art, the method has the advantages of high diversity, dynamic adaptation, high quality and the like.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

Systems and methods are provided for enhancing the speech modality in a large language model (LLM) and for retaining in-context learning capabilities without overfitting to trained tasks. Systems obtain a first set of training data comprising tuples of a sample of speech combined with synthetically generated pairings of speech comprehension test questions and answers that correspond to the sample of speech and obtain a second set of training data comprising pairings of automatic speech recognition data. Systems generate and align a first set of encodings of the first set of training data and a second set of encodings of the second set of training data. Systems train the LLM on a greater amount of the first set of training data than the second set of training data and use the trained LLM to perform a natural language processing task.

The invention provides an intelligent English question setting method and system based on a large model, and the method comprises the steps: obtaining all kinds of information in a teaching material, and generating a knowledge graph; extracting a dynamic knowledge graph; and constructing a question generation constraint, and generating test questions in combination with the dynamic knowledge graph. Through hierarchical and classified management and a dynamic screening mechanism of the knowledge graph, contents related to a current teaching target can be rapidly extracted, coverage and reasonable application of knowledge points are ensured, meanwhile, a new target can be rapidly adapted according to change of teaching materials, topic generation is restrained through the dynamic knowledge graph, and the teaching efficiency is improved. The generated questions are limited in the knowledge range of the students, the difficulty is dynamically adjusted according to the layering of knowledge points, the questions better conform to the teaching outline and the learning level of the students, teaching objectives can be effectively served, and meanwhile, the questions are subjected to multi-dimensional optimization by using a context enhancement and automatic evaluation mechanism, so that the teaching efficiency is improved. The method ensures that the questions are standard in language and clear in logic, and interference items of the selected questions are confused, so that the thinking investigation effect of students is improved.

The invention provides a clinical thinking examination question generation and step-by-step analysis construction method and system based on a large language model, and relates to the technical field of large language models. The method comprises the steps of analyzing a teaching outline and real questions over the years, constructing a multi-level clinical knowledge graph, intercepting a knowledge sub-graph according to target difficulty, and generating case question stems, candidate options and a preliminary reasoning path covering the sub-graph; the reasoning path consistency is verified through a logic engine, a simulation answer sample is constructed, item reaction modeling is executed, and the question difficulty is estimated; if the difficulty does not accord with the target difficulty, parameters are automatically fine-tuned and re-generated, and iteration is carried out until the standard is reached; performing error selection rate driven optimization on the interference options to form a final question version; and collecting student answering data feedback atlas weight and difficulty, and writing the content into the versioned question bank. According to the invention, the accuracy of examination question generation, the teaching suitability and the continuous updating ability are improved, and the intelligent evaluation of clinical thinking ability is realized.

The invention relates to the technical field of education, in particular to a visual chart system for analyzing test question quality and test object ability, which comprises a dynamic data preprocessing module, a sliding window adaptive adjustment module, a multi-axis fluctuation intensity analysis module, a group trend disassembly module and an abnormal test question judgment module. According to the method, a difference absolute value algorithm is adopted to construct an adjacent test question physical correlation model, a test question serial number is mapped as a time axis variable to generate a thermodynamic diagram, the time-space continuity of score fluctuation is captured, a sliding window algorithm is adopted to establish dynamic correlation between window density and fluctuation amplitude, and normalized parameter difference degree is matched with sampling frequency and data features. A three-axis direction change rate is defined to construct a three-dimensional radar coordinate system, Kriging interpolation is combined to generate a distribution cloud picture, group trend departure and standard deviation distribution difference are analyzed, spatial autocorrelation modeling is used to enhance the anomaly recognition capability, and time sequence modeling, dynamic feedback and spatial vector analysis are used to cooperatively improve the detection and evaluation precision and reduce the misjudgment risk.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses an evaluation method and device for a model reasoning effect on an electric power system, and relates to the technical field of electric power field rescue. According to the main technical scheme, for a large language model for performing reasoning operation on power data on a power system, a plurality of evaluation indexes are constructed for the model, and at least one evaluation index is randomly selected to be combined to obtain a target evaluation index set; an evaluation dimension is created for the target evaluation index set in combination with an actual application scene, so that test questions are constructed for the target evaluation index set on the basis of the evaluation dimension, and each test question has a preset correct answer; the method comprises the following steps: inputting target power data required by test questions into a model for processing, outputting reasoning result data given by the model, comparing the reasoning result data with a preset correct answer of each test question, and evaluating to obtain an evaluation result of a model reasoning effect.

The invention discloses a medical theory examination intelligent test paper composition method based on an artificial intelligence technology, and the method comprises the following steps: constructing a medical question bank, determining an influence vector of each test question in the medical question bank, and constructing a plurality of sub-question banks based on the influence vectors; constructing a test paper structure model, optimizing the test paper structure model through multi-target constraint, extracting and caching test questions from the sub-question bank, and performing intelligent test paper composition on the cached test questions; checking and proofreading the test paper formed by the intelligent test paper composition, and performing secondary intelligent test paper composition on the cached test questions based on a proofreading result; through the intelligent test paper composition method, the influence of human factors on the test result is reduced, the fairness and authority of the test are ensured, and the system can carry out personalized adjustment according to the actual level and demand of the examinee, so that the test better meets the actual demand of the examinee.

The invention relates to the technical field of teaching quality, in particular to an AI-driven classroom real-time interaction and teaching quality intelligent optimization system and method.The AI-driven classroom real-time interaction and teaching quality intelligent optimization system comprises a classroom test terminal used for determining student states, retrieving related knowledge points to generate classroom test questions and outputting answer results to generate a review report; the analysis and evaluation module is used for judging the current learning hidden danger type of the classroom according to the number of students meeting the classroom concentration condition; the interaction strategy module is used for determining a corresponding teaching interaction strategy according to the learning hidden danger type; the feedback optimization module is used for determining the memory difficulty coefficient of the test questions, correspondingly adjusting the test question quantity and the test question type proportion, judging the memory absorption degree and the understanding degree of the student according to the test question type, and correspondingly generating a teaching quality optimization reference; and adjusting the judgment standard for judging the class concentration condition and the attention concentration degree according to the question answering result of the test question type. The teaching strategy is adjusted in real time according to the classroom state and the learning condition.

The invention provides a self-adaptive question bank test paper formation and difficulty evaluation method and system based on a knowledge point tree, and relates to the technical field of data processing, and the method comprises the steps: constructing a knowledge point relation tree, calculating a knowledge point path to group and cluster test questions, building a multi-layer test question index structure, and calculating a real-time difficulty correction coefficient in combination with historical answer data of examinees; dynamically adjusting the priority of the test questions, and finally screening and generating target test paper according to the distribution proportion of knowledge points. According to the method, personalized test paper composition is realized, the accuracy of test question difficulty evaluation and the rationality of test paper knowledge point coverage are improved, and the requirements of adaptive learning and accurate evaluation are met.

The invention relates to the technical field of computers, in particular to a model confidence evaluation method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining an information evaluation set; inputting test questions in the information evaluation set into the first model and the second model respectively to obtain to-be-evaluated answers output by the first model and reference answers output by the second model; inputting the reference answers and standard answers of the test questions in the information evaluation set into a pre-trained evaluation model, and outputting first deviation information between the reference answers and the standard answers; when determining that the test question and the standard answer meet a preset confidence condition based on the first deviation information, inputting the to-be-evaluated answer and the standard answer into an evaluation model, and outputting second deviation information between the to-be-evaluated answer and the standard answer; generating a confidence evaluation report of the first model according to the second deviation information; according to the scheme, the confidence test of the test questions and the standard answers is realized, and the universality and the mobility of the evaluation method are improved through the arrangement of the evaluation model.

The invention provides an intelligent paper marking system based on a large language model. The intelligent paper marking system comprises an examinee test paper character recognition module used for carrying out image processing and content recognition on scanned or shot student answer sheets or answer sheets; the subject knowledge base is used for performing systematic arrangement and representation modeling on multi-subject teaching contents; the subject knowledge retrieval module is used for performing semantic analysis and matching on test paper questions and examinee answering contents to obtain subject knowledge related to the test questions and a scoring basis; a scoring template generator module; a large language model scoring module; and the comment correction module is used for optimizing and adjusting the preliminary comments generated by the large language model and outputting final comments with more pertinence and teaching guidance significance. The technical scheme can be widely applied to automatic evaluation scenes of subjective questions in the education field.

The invention relates to an A / B test question answering method and device based on a large model agent, and relates to the technical field of large models, agents and computers. The method comprises the following steps: displaying an intelligent interaction page associated with an intelligent agent; displaying a plurality of prompt word options in response to a trigger operation on a prompt word bank in the intelligent interaction page; in response to a trigger operation on a target cue word option in the plurality of cue word options, at least displaying a target cue word template corresponding to the target cue word option; in response to a content editing operation on the target cue word template, filling a target content corresponding to the content editing operation into the target cue word template to obtain a target cue word; and in response to a sending operation on the target cue word, displaying a target answer of the intelligent agent to the target cue word through the large model on the intelligent interaction page. The expression logic of the user for the intention can be optimized through the cue word, the content quality of questions is improved, then the accuracy of answers generated by the large model is improved, and the use experience of the user is improved.

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.

The invention provides a network security situation real-time perception and co-processing method, which comprises the following steps of: acquiring a test question downloading log in a campus network, extracting downloading time, user identity, course information and downloading quantity from the downloading log, and discretizing the extracted data to form a scattered batch initial behavior data set; analyzing the early warning signal and the sandbox environment security log, and if the resource occupancy rate suddenly rises, the authority is frequently changed and the network traffic is soaring, judging that the sandbox boundary is maliciously broken through, and obtaining a breakthrough feature analysis result; and dynamically adjusting a sandbox security policy according to the breakthrough feature analysis result, wherein the sandbox security policy comprises limiting the downloading permission of an abnormal anonymous account, isolating soaring malicious traffic, optimizing the allocation of swell resources, and applying the dynamically adjusted sandbox security policy to the sandbox.

The invention relates to the technical field of hydrological survey skill training, in particular to a construction method and system of a hydrological survey knowledge model based on a large model. The method comprises the following steps: acquiring multi-source hydrological data, and constructing a hydrological knowledge graph; generating a confusion sample corresponding to the hydrological knowledge graph, and performing adversarial training on the confusion sample; according to the confrontation training result and historical test questions in the multi-source hydrological data, obtaining bidirectional mapping test questions; inputting the bidirectional mapping test questions into a question setting interface of a hydrological survey learning platform; collecting answer data and student physiological data corresponding to the bidirectional mapping test questions in real time, and updating a student knowledge state according to the answer data and the student physiological data; constructing a hydrological survey knowledge model based on the updated student knowledge state; and generating a student recommendation learning scheme by using the hydrological survey knowledge model. According to the invention, the intellectualization and practicability of hydrological survey education can be improved.

The embodiment of the invention discloses a technical aspect test question generation method and device based on an AI Agent and a medium. The method comprises the following steps: inputting post description and candidate resumes, and processing by using NLP and AI Agent to generate original technical tags; inferring a project technical architecture by using an AI Agent; performing technical label generation and expansion based on the original technical label and the project technical architecture to obtain a full-quantity technical label; and generating technical face test questions and interview feedback. The method has the following advantages: the technical label highly matched with the post is generated based on the NLP and the AI Agent, and the technical label is expanded, so that the accuracy and efficiency of the recruitment process can be remarkably improved; face test questions are generated according to the technical labels, and correlation and pertinence of the face test questions are ensured; the recruitment process can be dynamically fed back and optimized according to an interview result; the human resource management cost is remarkably reduced, and meanwhile the recruitment quality is improved.

Provided in the embodiments of the present application are a large-model performance assessment method and a related device. The method comprises: acquiring structured text description of a test material, wherein the structured text description comprises information of the test material in a plurality of dimensions; inputting the structured text description and a test question into a large language model, so as to acquire an evaluation true value; inputting the test material and the test question into a large model under test, so as to acquire an answer under test of the test question; and inputting the evaluation true value, the test question and the answer under test into the large language model, so as to acquire an assessment result for the answer under test.

The invention discloses an AI intelligent proposition method, and belongs to the technical field of intelligent proposition, and the method comprises the steps that a proposition platform end is established by a platform side, and the proposition platform end comprises an industry large model training module, a professional knowledge base construction module and a user analysis module; the platform side establishes a user side for a corresponding user according to the service demand data, wherein the user side comprises a test question generation module, a user professional library and a test question checking module; the user professional library is used for storing industry knowledge information of service industries corresponding to users; the test question generation module is used for generating corresponding first draft test questions according to proposition requirements of a user and a professional library of the user; the test question checking module is used for automatically checking the generated first draft test question; the method comprises the steps of obtaining proposition requirements of a user, analyzing the proposition requirements through a test question generation module, obtaining first draft test questions, checking the first draft test questions through a test question checking module, marking the first draft test questions passing the checking as application test questions, and displaying the application test questions to the user.

The invention discloses a test question management method based on multi-modal adaptive similarity learning, and relates to the technical field of data management, and the method comprises the steps: collecting an original test question set, generating an evolutionary test question set through a preset large language model, generating a variable test question set through a data enhancement technology, and carrying out the fusion processing of the test question set to form a test question ecological library, the diversity and coverage of test question resources are enriched; the test question ecological library is input into a preset semantic similarity model and a preset syntactic similarity model to obtain a semantic similarity score and a syntactic similarity score of each test question, so that the similarity of the test questions can be comprehensively measured, and the screening accuracy is improved; the multi-modal similarity score is obtained according to the semantic similarity score and the syntactic similarity score to be compared with the preset threshold value, then the test questions are imported into the auditing module or the question bank storage module according to the comparison result, intelligent distribution of the test questions is facilitated, and the timeliness and quality of the question bank are ensured.

The invention discloses a cognitive function evaluation method and device, equipment, a medium and a product, and relates to the technical field of computers.The method comprises the steps that various forms of questions in cognitive tests are classified, and a question set corresponding to each cognitive function category is obtained; determining the importance of each question in each form of cognitive test according to the data set; the data set comprises the score of each question when various forms of cognitive tests are respectively performed on each subject; determining an optimal question set corresponding to the cognitive function category according to the importance of each question in a question set corresponding to the cognitive function category; testing the testee according to the preferable question set corresponding to each cognitive function category to obtain a score of each question in the test question set by the testee; and obtaining an evaluation result of the cognitive function of the testee according to the score of each question in the test question set by the testee. According to the method and the device, the cognitive function can be evaluated conveniently and quickly.

The invention discloses a test question data generation method, electronic equipment, a storage medium and electronic equipment, and relates to the field of large model technology and text data processing. The method comprises the steps that in response to a first input instruction acting on an operation interface, a target document corresponding to the input instruction is displayed on the operation interface, and the target document comprises multi-modal data; in response to a conversion instruction acting on the operation interface, displaying target text data corresponding to the conversion instruction on the operation interface; in response to a second input instruction acting on the operation interface, a generation condition corresponding to the second input instruction is displayed on the operation interface, and the generation condition comprises at least one of a target type, a target difficulty level and a target dimension; and in response to a generation instruction acting on the operation interface, displaying the target test question data on the operation interface. The technical problem of low efficiency and diversity of test question data generation in related technologies is solved.

To provide an examination question creation support system for supporting creation of examination questions with adjustable difficulty levels.SOLUTION: Provided is an examination question creation support system for supporting questions in a mock examination simulating a real examination, including multiple-choice questions that require a student to select a correct answer from a prescribed number of options described in each sentence, and the system includes: an option sentence management unit that when a learning item related to an option sentence, the sentence describing an option, is an item to be mastered in order to pass a real examination, associates identification information of the learning item with the option sentence and stores them as option sentence management data; and a question creation unit that extracts option sentences from the option sentence management data so as to include option sentences, the number of which is determined according to a required first difficulty level and to which the identification information is not added, and creates questions including options based on each of the extracted option sentences.SELECTED DRAWING: Figure 15

The invention relates to the technical field of auxiliary paper marking, and particularly provides an intelligent paper marking method based on a large language model and optical character recognition. The method comprises the steps that test paper and answers are processed through a large language model, test question analysis texts are obtained, and the test question analysis texts comprise analysis texts of the answers; processing the test paper image needing to be marked through an optical character recognition technology to obtain a corresponding answer text; and through comparison of the test question analysis text and the answer text, score data of each question is determined, and the score data comprises scores and comments. According to the intelligent paper marking method provided by the invention, the problem of low paper marking accuracy of an intelligent paper marking method in related technologies can be solved.

The invention discloses an intelligent teaching method, system, device and equipment and a readable storage medium, and relates to the technical field of intelligent education, and the method comprises the steps: obtaining the state performance characteristics of a student during the course learning; predicting the efficiency value of the learning efficiency of the student according to the state performance characteristics, and adjusting the difficulty of the course learned by the student in real time when the difference between the efficiency value and a preset threshold value is greater than a preset difference value; according to the course with the difficulty adjusted, a test question corresponding to the course with the difficulty adjusted is called, and in the teaching process, according to the real-time playing progress of the course with the difficulty adjusted, the test question is released so that the student can test the learning progress in real time; and adjusting the difficulty of the course learned by the student in real time according to the completion condition of the test question by the student. The effect of automatically adjusting the difficulty of the learning course in the learning process according to the learning condition of the student is achieved.