574 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.

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 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 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 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 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.

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 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.

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 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.

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 junior high school history auxiliary learning method and system based on a knowledge graph, and belongs to the technical field of learning resource management and recommendation, and the method comprises the steps: obtaining junior high school history teaching data; the sources of the junior high school historical teaching data comprise a junior high school historical textbook, a test question book, an educational resource platform and a knowledge base; constructing a junior high school historical knowledge graph mode layer based on the junior high school historical teaching data; preprocessing the junior high school historical teaching data; constructing a junior high school historical knowledge graph data layer based on the preprocessed junior high school historical teaching data and the junior high school historical knowledge graph mode layer; and assisting students in learning based on the junior high school historical knowledge graph data layer. According to the invention, the informatization and intelligence level of history teaching in junior high schools can be improved.

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 student ability detection platform for education and teaching, belongs to the technical field of low-pressure casting machines, and aims to solve the problem that the existing student ability test cannot perform personalized accurate test according to the actual mastering condition of each chapter unit of a student. Through the test question bank storage module, by means of subject unit classification, knowledge point labeling association and question type difficulty management and control, a rich question type and difficulty system is combined, a teacher can conveniently and accurately select questions and form test papers, the purpose of providing scientific and reasonable test content for teaching is achieved, the historical data storage module keeps comprehensive score data and performs deep analysis, and the test efficiency is improved. The test question extraction module defines a state space, an action space and a reward function based on a reinforcement learning algorithm and according to historical answer data of students, and continuously optimizes a test question selection strategy, so that test questions closely meet actual demands of the students, and the effect of accurately detecting knowledge mastering conditions of each chapter unit of the students is achieved. And education and teaching quality improvement is effectively promoted.

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 discloses an automatic generation method and device of operation and maintenance test questions, a medium and equipment. The invention belongs to the technical field of computers. The method comprises the following steps: acquiring collected data of a target domain, and constructing a vector knowledge base and a knowledge graph of the target domain; obtaining a question setting demand input by a user, and matching the question setting demand in the vector knowledge base and the knowledge graph to obtain an association vector and an association entity relationship set; determining a first weight and a second weight; according to the association vector, the first weight, the association entity relationship set and the second weight, generating a text block after knowledge fusion; and calculating the confidence of each text block according to the difficulty of each text block, screening the text blocks based on the confidence, and inputting the screened text blocks into the large model to obtain operation and maintenance test questions. According to the scheme, the text blocks after knowledge fusion can be generated based on the vector knowledge base and the knowledge graph, the text blocks are screened and then input into the large model, and a more accurate operation and maintenance test question output result is obtained.

The invention discloses a vertical field objective test data set construction method, system and device and a medium, and relates to the technical field of natural language processing, the construction method comprises the following steps: obtaining text data, and carrying out knowledge extraction and arrangement on the text data to obtain formatted text information; taking the formatted text information as input, and performing question generation, correct answer generation, interference option generation and analysis generation through a pre-established large model to obtain test data; performing quality detection on the obtained test data, and screening out the test data meeting the quality detection requirements; and performing self-inspection on the test data meeting the quality detection requirements, and retaining the test data passing the self-inspection to obtain a vertical field objective test data set. According to the method, the vertical domain test questions can be automatically generated on a large scale, the dependency degree of testers on professional domain knowledge is reduced, meanwhile, the labor cost is saved, the problem difficulty is controllable, and the professionality and accuracy of test question generation are improved.

The invention relates to the technical field of knowledge tracking, in particular to a knowledge tracking method and system based on multi-modal auxiliary information extraction, and the method comprises the steps: obtaining learning interaction data, test question data containing graphic and text information of test questions, and a step-by-step solving process corresponding to each test question, and carrying out the auxiliary feature extraction to obtain an auxiliary information set; constructing a knowledge tracking model embedded based on an auxiliary information set, obtaining question embedding of test questions from learning interaction data of students, and embedding of answer activities covering knowledge concepts and answers of the students; establishing an attention network model, and based on question embedding and answering activity embedding, determining the degree of mastering the question and the knowledge state of the student; and performing data enhancement on the learning interaction data, enhancing representation consistency of knowledge states before and after by utilizing comparison loss constraint data, and predicting subsequent answering performance based on the current knowledge state of the student. Through multi-modal information extraction and an attention network model, the knowledge state of the student is accurately evaluated, and the answering performance is predicted.