470 results about "Question generation" patented technology

The invention relates to a multi-agent cooperation strategy generation method, device and equipment and a medium, and the method comprises the steps: separating acoustic spectrum features and text semantic features of conference voice through environment perception processing, solving a cross-modal information conflict problem, and generating an accurate semantic understanding result; identifying the essence of the problem based on task analysis, associating the responsibility field, and constructing a classifiable problem point set; calling an agent capability library to dynamically match problem requirements, and generating a candidate agent list; quantifying a problem influence range and a decision time limit through weighted emergency scores, and generating a priority-sorted agent sequence; screening and confirming a core problem point and a primary agent; and finally, generating an executable cooperation scheme through multi-agent collaborative optimization. According to the method, the problems of incomplete feature extraction, task allocation delay and resource conflict in the prior art are solved, and the operability and decision-making efficiency of a cooperation strategy are remarkably improved.

The invention discloses a progressive topic generation method based on semantic analysis and a knowledge graph. Performing preprocessing and semantic analysis on the question setting demand text, and extracting a necessary keyword set corresponding to the core knowledge points and an optional determiner set corresponding to the additional conditions; carrying out concept mapping in a college professional knowledge graph and associating with a course outline, constructing a hierarchical semantic constraint framework containing hard constraint and soft constraint, and carrying out consistency detection; adopting reverse index hard matching recall and knowledge graph soft extension recall to obtain candidate materials, and inputting the candidate materials into a field fine-tuning large language model to generate candidate questions; reordering is performed through multi-target learning ordering, teaching logic verification and quality evaluation are executed, and final questions are output; user feedback is received to form an incremental sample, and the generation model and the sorting model are updated, so that the accuracy, diversity and controllability of question generation are improved, and closed-loop optimization is supported.

The invention relates to an automatic full-process intelligent recruitment system based on competency evaluation, and belongs to the technical field of intelligent interview. The system comprises a data acquisition module, a question generation module, an interactive execution module, a dynamic questioning module, a dimension switching module, an anomaly monitoring module, a dual-camera verification module, a score report module and a context maintenance module. Through cooperative work of the modules, the system can realize full-process automation from candidate data collection to final scoring report generation. The data acquisition module is responsible for collecting various information of candidates and providing basic data for subsequent interview links. Automation, intelligentization and standardization of the recruitment process are realized, and the recruitment efficiency and quality are effectively improved. By generating interview questions and dynamic questions in real time, the system can more accurately inspect ability performance of candidates in different competency dimensions.

The invention provides an agent illusion correction method and device. The method comprises the following steps: firstly, analyzing and globally planning a problem input by a user, generating an execution plan comprising at least one subtask, performing reasoning circulation and retrieval enhancement based on the execution plan, generating an enhanced context evidence set, then performing screening and context optimization processing on the enhanced context evidence set, generating an optimized context, and finally, performing context optimization processing on the optimized context. And then, generating an initial answer based on the optimized context and the question, carrying out factuality and logic consistency closed-loop verification on the initial answer to detect whether illusion exists or not, if the illusion is detected, correcting the initial answer, generating a corrected answer, and recording the illusion event and the correction process to the reflection knowledge base. According to the method, self-correction and continuous optimization are realized through a closed-loop mechanism of problem decomposition, global planning, reasoning circulation, retrieval enhancement and optimization and correction reflection, so that the reliability and interpretability of agent answering are improved.

The invention relates to the technical field of human resource management, and provides a one-stop talent screening method and system. The method comprises the steps of performing text analysis on a job seeker resume, identifying structured information in the job seeker resume, performing weight distribution on the analyzed information through a preset post competency model, and generating a resume analysis result containing a candidate and target post matching degree score; consultation information about enterprise information, position treatment and office environment of the job seeker is obtained, the consultation information is analyzed to obtain a question intention of the user, and response content corresponding to the question intention is generated; performing semantic analysis on the communication record corresponding to the job seeker, and identifying the job hunting willingness level of the job seeker; and dynamically generating personalized interview questions through an interview question generation model in combination with the matching degree score and the job hunting willingness level to complete talent screening. The method is suitable for full-process automatic processing of resume screening, intelligent communication and adaptive interview in the enterprise recruitment process.

The invention provides a vertical field data construction method based on a large model, which belongs to the technical field of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text, and segmenting the intermediate format text into a plurality of text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model according to pre-designed cue words to generate a plurality of candidate questions according to the content of the text blocks, and performing preliminary screening and fine screening on each candidate question to obtain a question set; pre-defining a mode of a knowledge graph according to field characteristics of the vertical field, processing all text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval on each final question in the question set based on the text block of the question source, performing global knowledge retrieval based on the domain knowledge graph, and generating a final answer and a final thinking chain. The method is suitable for different vertical fields, the data quality can be effectively improved, and the problem generation accuracy is guaranteed.

A computerized method has the steps of: generating one or more queries from an input question; generating one or more outputs; and outputting an answer based on the one or more outputs. Said generating the one or more outputs has the steps of: for each query, forming a reasoning tree with the query being a root node and a current node, generating one or more candidates as leaf nodes of the current node, by inputting a reasoning path from the root node to the current node into a foundation model, searching the reasoning tree using an artificial intelligence model to select a leaf node as the current node, and repeating said generating the one or more candidate nodes and said searching the reasoning tree until a termination condition is met, and using an updated reasoning path from the root node to the current node as the output for the query.

The invention provides a big language model knowledge graph question answering method and system combined with semantic correction, and the method comprises the steps: generating a logic form according to an input question through a fine-tuned open source big language model, and executing a query statement corresponding to the logic form to obtain an answer; if the answer cannot be obtained by the query statement corresponding to the execution logic form, extracting a main line entity and a relationship from the non-executable logic form, and performing semantic similarity comparison on the extracted relationship and a relationship in the knowledge graph through an unsupervised dense searcher to generate a candidate relationship set; based on the main line entity and the candidate relation set, iteratively retrieving triples in a knowledge graph to construct a reasoning path; and screening the constructed reasoning path by using a closed source large language model, or selecting a tail entity in the multi-answer path, and outputting a final answer. According to the method, the non-executable logic form is corrected, the semantic analysis effect based on the large language model is optimized, and efficient knowledge graph question answering is achieved.

The embodiment of the invention discloses a question answering method and device based on retrieval enhancement generation, equipment and a medium, and relates to the technical field of large language model question answering. The method comprises the steps of obtaining a target question input by a user; on the basis of keywords in the target question, keyword retrieval is carried out on each text in a pre-established knowledge base; each text in the knowledge base corresponds to a text vector; retrieving a text vector similar to the target vector in a pre-established knowledge base, and determining a text corresponding to the retrieved text vector; the target vector is a vector generated based on a target problem; and processing the target question and a retrieval result obtained in the knowledge base based on the large language model to obtain an answer corresponding to the target question. According to the technical scheme, text retrieval and vector retrieval are carried out in the knowledge base, the retrieval result with higher timeliness and higher accuracy is obtained, and then the answer output by the large language model can be obtained based on the retrieval result and the target question.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

Techniques for generating a natural language prompt to further a goal of a dialog, are described. During a dialog, the system receives one or more user inputs including a user question, a user response to the question, and a request to generate a further question following the response. The system determines ASR output data corresponding to the user inputs, and determines dialog history data of the dialog. Using the ASR output data and the dialog history data, the system determines a category and an explanation of relevance corresponding to the category. Using the ASR output data, the dialog history, the category, and the explanation, the system determines the further question to be output to the user.

The invention discloses a robot interactive target object grabbing system and method, and the system comprises a visual positioning module which is configured to obtain a current scene image and an initial grabbing instruction, and carries out the prediction to obtain a candidate region coordinate set of a to-be-grabbed target object; the question generation module is configured to sample a candidate region coordinate from the candidate region coordinate set as a candidate target region, and generate a corresponding natural language question based on the scene image; the answer understanding module is configured to score the matching degree of each candidate area in response to the answer of the user to the natural language question; and the collaborative reasoning module is configured to integrate the probability of the coordinate of each candidate region obtained by the visual positioning module and the matching degree score of each candidate region by the answer understanding module, and output a final target region position. According to the method, the limitation that a traditional method depends on a preset template and an explicit category is broken through, and the target object to be grabbed by a user can still be accurately recognized in a complex scene.

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

The invention provides a wrong question book intelligent learning system and method based on OCR and similarity analysis. The method comprises the following steps: S1, carrying out wrong question picture preprocessing and feature extraction; s2, multi-dimensional similarity calculation is associated with knowledge points; s3, knowledge point error rate statistics and error pattern analysis; and S4, generating and pushing personalized exercises. According to the method, intelligence and individuation of error question management are realized through multi-technology fusion, the bottleneck of current error question management is broken through, and the learning efficiency and the knowledge mastering depth are improved.

The invention discloses a reward model training method, a question evaluation method, an electronic device and a storage medium, and relates to the technical field of large models.The reward model training method comprises the steps that a sample data set and a target cue word are obtained, and the sample data set comprises one or more text samples; inputting the target prompt word and the sample data set into a first question generation model, and generating a first question corresponding to each text sample; inputting the target prompt word and the sample data set into a second question generation model, and generating a second question corresponding to each text sample; wherein the problem generation capability of the first problem generation model is superior to the problem generation capability of the second problem generation model; constructing comparison data based on the text sample, the target prompt word, the first question and the second question; performing comparative learning on a preset reward model based on the comparative data to obtain a target reward model; wherein the target reward model is used for evaluating the problem quality. According to the invention, the quality evaluation problem can be solved.

The embodiment of the invention discloses a strategy model training method and device, a strategy generation method and device, a storage medium and a terminal. Firstly, a set of sample answers are generated for a target question through a preset large language model to serve as references. And during training, inputting an answer generated by the current strategy model and a sample answer into the evaluation model for comparison, and outputting a good and bad sorting result. The ranking is quantized as a reward value whose size is positively correlated with the degree to which the current answer is superior to the sample answer. And finally, the system adjusts strategy model parameters according to the reward value and guides the strategy model parameters to be continuously optimized. According to the method, scoring according to standard answers is replaced with relative sorting, and the problem that open questions lack clear judgment standards is solved. The method has the beneficial effects that the training data cost and labeling dependence are remarkably reduced, so that the model can realize stable and autonomous efficiency improvement in the vertical field, and a self-driven benign evolution cycle is formed.

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 invention discloses a retrieval enhancement generation-based big language model personalized question and answer method, which comprises the following steps of: receiving a dialogue request of a client, and analyzing the dialogue request into personal condition description and a specific question; sending the specific question to a pre-constructed user portrait knowledge retrieval enhancement database, obtaining retrieval enhancement data containing a user portrait, and combining the retrieval enhancement data with the personal condition description into context retrieval enhancement data containing the user portrait; and according to the context retrieval enhancement data containing the user portrait and the specific question, generating a question instruction and inputting the question instruction into a large language model to obtain corresponding answer feedback data. According to the method, individual condition description and specific questions are analyzed, retrieval enhancement is carried out through the user portrait knowledge retrieval enhancement database, personalized adaptation of large language model questions and answers can be achieved, and the problem that in the prior art, the personalized degree of a large language model is not high is solved.

The invention discloses a job-hunting interview question AI generation method based on a large language model, and relates to the technical field of human resource management, and the method comprises the following steps: S1, analyzing post information, and extracting core skill points; s2, constructing candidate feature matching vectors based on the core skill points; s3, performing knowledge graph expansion by using the core skill points and candidate feature matching vectors; s4, performing semantic fusion on the basis of the extended knowledge graph to obtain a knowledge association set; s5, performing question generation and difficulty grading by using the knowledge association set; and S6, performing logic consistency detection and diversification generation by using the question set. By setting knowledge graph extension and semantic fusion based on post core skill points and candidate feature matching vectors, a knowledge association set with a wider coverage and a more reasonable structure can be established before topic generation, and it is ensured that the extended knowledge graph not only embodies post requirements but also is matched with candidate features.

The present disclosure provides a large model-based recommendation question generation method and device, electronic equipment and medium, which relates to the technical field of artificial intelligence, and in particular to the technical fields of natural language processing, large language model and intelligent customer service. The specific implementation scheme is: receiving a current question input by a user, and generating a current answer for the current question. Then, the historical questions input by the user, the historical answers generated for the historical questions, the current question and the current answer are used as context information. Then, a large language model is used to generate a recommended question based on the context information. The diversity of the recommended question is enhanced.

The invention discloses a multi-round dialogue system and method based on conversion from a natural language to an SQL, and relates to the technical field of artificial intelligence, and the system comprises a user interaction module which is used for supporting a user to input a query problem and displaying a corresponding SQL query result; the context processing module is used for screening the effective dialogue history of the current question and re-integrating the effective dialogue history into a complete dialogue; the ambiguity processing module is used for identifying an entity from a dialogue history to define a user intention, processing a fuzzy keyword in a current question and guiding the user to complement a query condition; the re-splicing module is used for reconstructing a complete dialogue according to a time sequence, eliminating ambiguity, generating a summary through a large model, and combining the summary with a current problem to construct a complete problem; the SQL generation module is used for performing semantic analysis on the complete problem and outputting an SQL query instruction with the highest matching degree; and the execution and rendering module is used for executing the instruction on the target database and returning a query result. The conversation processing capability can be improved.

The invention relates to the technical field of artificial intelligence, and provides a domain relation extraction method and system based on a large language model. The method comprises the following steps: identifying entity information in a field text set, and obtaining a labeled entity set with type labels; on the basis of entity pairs in the labeled entity set and predefined relation types, a question set is constructed by utilizing a judgment question generation algorithm, and a domain relation judgment question set is obtained; reconstructing the structured domain data into training data through a dialogue format conversion algorithm, and training a large language model based on the training data by adopting a QLoRA quantization fine tuning algorithm to obtain a domain fine tuning model; a double-layer retrieval algorithm is applied to retrieve and obtain related information from the knowledge graph, the domain relation judgment question set and the related information are combined and input into the domain fine tuning model for reasoning, and a domain relation triple is obtained. According to the method, high-precision and interpretable domain relation extraction is realized, and the accuracy and robustness of the model in the vertical domain are improved.

This disclosure provides a training method, a recommendation question generation method, and an apparatus for a recommendation question generation model, relating to the fields of artificial intelligence technology, particularly deep learning, large language models, and intelligent dialogue technology. The implementation scheme comprises: obtaining a first recommendation question; determining that the first recommendation question meets a first screening condition, determining the first recommendation question as a positive sample recommendation question; obtaining at least one sample data based on the positive sample recommendation question; and training an initial generation model based on the at least one sample data to obtain a recommendation question generation model.

The invention discloses a classroom content evaluation system based on student questions and answers, and the system comprises a classroom information collection module, a data processing module, a classroom question generation module, a student answer interaction module, a semantic analysis module, a classroom evaluation generation module, a classified storage module, and a teaching strategy optimization module. The classroom information acquisition module monitors and acquires videos, audios, blackboard-writing texts and courseware images in real time, the data processing module converts multi-modal data into structured texts through voice recognition and OCR technologies, and the classroom question generation module generates a question set based on teaching content. The student answering interaction module receives answering data in a text, voice or image form, and the semantic analysis module analyzes teaching texts and answering contents and marks knowledge blind areas; the system has the advantages that multi-modal teaching content is integrated in real time, adaptability problems are dynamically generated, knowledge blind areas are accurately recognized, and the classroom effect is quantitatively evaluated in a multi-dimensional mode.

The embodiment of the invention provides a question and answer data processing method and related equipment, and belongs to the technical field of natural language processing. The method comprises the following steps: carrying out hierarchical structure semantic division processing on a preset reference file, then carrying out semantic vectorization according to a preset text block obtained by division, and constructing to obtain text vector data; constructing a text vector retrieval model through a preset retrieval ranking algorithm according to the text vector data; splitting the preset user question through a preset large model to obtain a plurality of target sub-questions; according to the text vector retrieval model, performing retrieval sorting processing on each target sub-problem to obtain a target retrieval result; and generating a target question and answer result according to the target retrieval result and the preset user question. According to the embodiment of the invention, the understanding ability of the model for the user question can be effectively improved, and the accuracy of model answering is effectively improved.

The invention discloses a security and robustness automatic testing method for a large language model in the medical field, and the method comprises the steps: constructing a field perception harmful problem generator which can automatically learn and map medical knowledge to potential harmful queries; according to the method and the system, the real security boundary of the large language model in the medical field can be efficiently and systematically evaluated, and particularly, the evaluation depth and the evaluation breadth are provided in complicated situations involving professional knowledge misleading or potential harm. A generator is optimized through a customized weighted composite reward function, so that the model is effectively guided to generate a field perception harmful problem which has high harmfulness, is coherent and smooth and conforms to grammar, and the quality and pertinence of a data set are remarkably improved; according to the framework, the dependence on a large amount of time-consuming manual intervention is fundamentally eliminated by integrating automatic scoring and a multi-index similarity filtering mechanism of a hazard detection model.

A method of using a computing device to improve an answer generated by a natural language question and answer system includes receiving, by a computing device, multiple questions in a natural language question and answer system. The computing device further generates multiple answers to the multiple questions. The computing device still further constructs a new training set with the generated multiple answers, where each answer is compared with a corresponding question of the multiple questions. The computing device additionally augments the new training set with one or more tokens delimiting a span of one or more of the generated multiple answers. The computing device further trains a new natural language question and answer system with the augmented new training set.

The invention discloses a self-adaptive interview simulation system based on LLM. Comprising an initial interview question generation module used for acquiring and analyzing background information of a user and generating an initial interview question, an interview main process module used for generating feedback and a new question according to the answer of the user, and an interview ending module used for judging whether the interview is completed or not according to the dialogue round and the coverage degree of the answer of the user and generating an evaluation report. The interview process is divided into three stages of starting, question answering and ending, automatic closed-loop management is realized through logic judgment, and closed-loop interview logic is formed; full-process closed loop from initial problem generation to evaluation report output is realized, manual intervention is reduced, and full-process automation is realized; a structured report is generated according to user performance, and specialized evaluation is achieved; custom adjustment of interview logic is supported, multi-industry requirements are met, and flexibility and expandability are achieved; and full-process automation of combination and interaction of the initial problem generation module, the interview main process module and the end interview module is realized.

The invention discloses a multi-round intelligent dialogue question-answering method, device and system for automatic inspection, and belongs to the field of intelligent inspection, and the method comprises the steps: constructing a corresponding number of word slots and a corresponding number of auxiliary anchor points according to the number of scenes contained in a task at the beginning of the task, and then repeating the following steps: obtaining and analyzing the first-round input information of a user, extracting intention information and word slot information, and updating a task context according to the information; generating guide theme information and executing query operation according to the intention information and the updated task context, obtaining a query result and setting a current auxiliary anchor point; generating information needing to be fed back to the user according to the guide theme information and / or the query result, and generating a question needing to be proposed according to the guide theme information; and according to the information needing to be fed back to the user and the questions needing to be put forward, generating the next round of reasoning guide content. The task anchor points are set and managed in the scene-oriented dialogue task, and multiple rounds of dialogues are associated into one task.