828 results about "Question generation" patented technology

A user provides a question to be answered from detailed, dense or otherwise complex documents to a processing system that converts the question to a structured query language query and generates an embedding from the question, augmented by temporal data, synopses, themes, or other relevant information or data. The embedding is compared to embeddings generated from documents of a knowledge base to identify documents that are relevant to the question, and to rank such documents for their relevance. Highly ranking documents are combined with the query and provided to a language model that returns an answer to the question. A source for the answer is identified in at least one of the documents. The answer and the identified documents are presented to the user.

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention discloses a knowledge base generation and retrieval method based on the RAG technology and an intelligent question answering system, and belongs to the technical field of natural language processing, and the knowledge base generation and retrieval method based on the RAG technology and the intelligent question answering system based on the RAG technology have the beneficial effects that the accuracy and the real-time performance of information processing are improved through efficient combination of retrieval and generation. High-dimensional vector representation is generated through text slicing and vectorization, a vocabulary index is constructed, and hierarchical retrieval and keyword matching are achieved; meanwhile, a question generation and question-question retrieval method is utilized, a result is optimized and generated in combination with a large language model (LLM), the reasoning ability of the large model is further improved through chain reasoning (COT) and a cue word optimization module, and it is ensured that the generated answer has high accuracy and logicality; the method can be widely applied to the fields of intelligent question answering, knowledge base construction, real-time information retrieval and the like.

In various examples, synthetic question-answer (QA) pairs may be generated using question and answer generation models comprising corresponding language models (e.g., autoregressive LLMs). A repository of textual data representing a particular knowledge base may be used to source synthetic QA pairs by partitioning textual data from the repository into units of text (e.g., paragraphs) that represent context. For each unit of text, the question generation model may be prompted to generate a synthetic question from that unit of text, and the answer generation model may be prompted to generate a synthetic answer to the synthetic question. Textual entailment and / or human evaluations may be used to filter out low quality, incorrect, and / or non-productive QA pairs that may be a result of hallucinations. As such, the synthetic QA pairs may be used as, and / or may be used to generate, training data for one or more machine learning models.

The invention discloses an intelligent finance and tax question answering method based on an artificial intelligence question answering system, and the method comprises the steps: S1, generating a structured semantic representation through a natural language processing technology, and generating an intention tag through a machine learning algorithm; s2, constructing and maintaining a knowledge graph containing finance and tax field related information, storing the information in the knowledge graph in a structured manner, and reasoning by using a logical reasoning algorithm to generate a preliminary answer; s3, designing a document retrieval and indexing mechanism, and verifying and supplementing the preliminary answer; s4, training the system based on the finance and tax related data and question and answer pairs, and continuously updating the model; s5, identifying common finance and taxation question modes by using a mode identification technology, and providing standardized answers; and S6, supporting multiple rounds of dialogues with the user, and generating more detailed and accurate answers by gradually clarifying the questions of the user. The method has the advantages of high accuracy, high response speed and capability of continuously learning and adapting to new problems.

The invention discloses a large language model illusion detection-based medical question and answer method, which comprises the following steps: S10, data acquisition: acquiring a question, and generating a plurality of answers for the input question by using a large language model; s20, data preprocessing is carried out on the collected multiple answers given by the large language model; s30, the problem is processed by means of evidence deep learning, and a loss function of the evidence deep learning is obtained; s40, processing the generated pair answers by using structure entropy and enhanced semantic clustering to obtain a structure entropy loss function; and S50, knowledge boundary illusion detection: integrating the loss function of evidence deep learning and the structural entropy loss function to obtain a total weighted loss function, and judging an output final answer by setting a threshold value. According to the method, the big language model is optimized by combining the evidence deep learning theory and the structure entropy, so that the problem caused by the illusion phenomenon in the medical question and answer scene is effectively solved.

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.

SOLUTION: An answer feedback method to be applied to a large-scale language model includes the steps of: receiving a question inputted by a user; generating a candidate answer set for a question, selecting an answer from the candidate answer set as a target answer and displaying the target answer to the user by a pre-training large-scale language model; and generating a feedback page in accordance with the reception of a feedback request transmitted to the target answered by the user, and displaying the feedback page to the user. A content of the feedback page includes the candidate answer set. Also, the method includes a step of specifying an answer indicated by an update request in the candidate answer set as a new target answer, and displaying the new target answer to the user in accordance with the reception of an update request transmitted to the feedback page by the user.EFFECT: An explicit correct answer given from a user can be simply acquired, and the accuracy of an answer feedback result can be improved.SELECTED DRAWING: Figure 2

The invention provides a method and a system for generating an accurately referenced article based on a large model, and belongs to the technical field of article generation. The method comprises the following steps: acquiring an article generation demand input by a user, and generating an abstract of an article according to the demand by using a large model; retrieving knowledge related to the abstract in an RAG knowledge base based on the abstract; generating a series of question and answer pairs by utilizing a large model based on an RAG retrieval result; generating an outline of the article according to the title, the keyword and the question in the question-answer pair; performing importance evaluation on each chapter in the outline by using a scoring model, and calculating the number of words to be distributed to each chapter; splitting the generated outline according to the lowest-level title, combining the title, the keyword, the answer in the question and answer pair and the number of words allocated to the section, and inputting into a large model to generate corresponding text content; and combining the text contents according to an outline sequence to obtain a final article. According to the invention, word number control and accurate reference during article generation can be realized, the accuracy and reliability of article generation are improved, and the user experience is improved.

The invention relates to a reasoning data synthesis method and system, and the method comprises the steps: obtaining a mathematical big language model, and carrying out the question fine adjustment of the mathematical big language model through a question fine adjustment method by employing a preset data set in a model training process, and obtaining the mathematical big language model processed through the question fine adjustment method; generating a sample problem through the mathematical big language model processed by the problem fine tuning method; the sample problem is optimized through a problem preference optimization method, and then the optimized sample problem is used for training the mathematical big language model processed through the problem fine adjustment method; taking the mathematical big language model processed by the problem fine tuning method and the problem preference optimization method as a problem generator, and filtering problems generated by the problem generator; a corresponding answer is generated for each filtered question to synthesize inference data. The reasoning data synthesis method not only can ensure the data quality, but also can reduce the generation cost and improve the generation efficiency.

The invention provides a recommendation question generation method and device based on a large model, electronic equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of natural language processing, large language models and intelligent customer service. According to the specific implementation scheme, a current question input by a user is received, and a current answer is generated for the current question; and then taking a historical question input by the user, a historical answer generated for the historical question, the current question and the current answer as context information. And generating a recommendation problem based on the context information by using a large language model. The diversity of recommendation problems is enhanced.

A method of dynamically updating a frequently asked questions knowledge base for agent assist according to an embodiment includes receiving, by a computing system, a transcript of a contact center interaction between a contact center agent and a client, analyzing, by the computing system, the transcript to generate a plurality of question-answer pairs based on an artificial intelligence model, determining, by the computing system, whether each question-answer pair of the plurality of question-answer pairs is stored in a dynamic frequently asked questions knowledge base, determining, by the computing system, whether a question-answer pair of the plurality of question-answer pairs is frequently asked in response to determining that the question-answer pair is not stored in the dynamic frequently asked questions knowledge base, and updating, by the computing system, the dynamic frequently asked questions knowledge base to include the question-answer pair in response to determining that the question-answer pair is frequently asked.

The invention discloses a proposition method and system combining a large model and an inference engine, a medium and a product, and relates to the field of artificial intelligence. Matching knowledge point rules from a preset reasoning rule base; combining the knowledge point rules to obtain a knowledge point combination sample; reasoning the knowledge point combination topics through a reasoning engine to generate a formal language expression topic set; based on a preset translation template, converting the formal language expression question set into a natural language expression question set; rewriting the natural language expression question set through the large model to generate an optimized question set; judging whether each optimization question in the optimization question set meets a preset output condition or not; and outputting the optimization questions meeting a preset output condition in the optimization question set. By implementing the method, the logic accuracy of question generation is improved.

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.

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.

A large language model generation method based on retrieval enhancement belongs to the field of computers and comprises the following steps: retrieving similar document abstracts according to input questions, converting the input questions and the document abstracts into points in a high-dimensional vector space, retrieving related documents and converting the related documents into a structured knowledge graph, generating sub-questions and extracting key information from the sub-questions, then, calling a large language model to carry out few-sample learning so as to generate associative cue words; generating a query command in combination with the associative cue word and the sub-question; obtaining sub-knowledge graph information by executing a query command, converting the sub-knowledge graph information into a text, and compressing and converting the text; and inputting the converted text and the question into a GPT-4 large language model to generate a natural language answer. According to the method, multi-level information and complex data in complex questions can be processed more accurately, the accuracy, efficiency and quality in large-scale data set and complex document processing are improved, accurate and coherent answers are provided, and the operation cost and resource consumption are reduced.

The invention relates to the technical field of large models, in particular to a large model cue word automatic optimization method and device and electronic device.The large model cue word automatic optimization method comprises the steps that a question input by a user is obtained, and a plurality of candidate cue words are generated based on the question; performing effect evaluation on each candidate cue word by utilizing a large model, performing descending sorting on each candidate cue word according to an effect evaluation result to determine a plurality of optimal cue words, and storing the optimal cue words; carrying out iteration on the optimal cue word in the cue word bank, carrying out distillation evaluation on the optimal cue word after iteration, and if a distillation evaluation result reaches a set threshold value, updating the number of iterations; and performing descending sorting on the optimal cue words completing the set number of iterations according to the distillation evaluation result, and outputting the first optimal cue word as a final cue word. According to the method, the cue word can be automatically optimized, the cue word quality is improved, and the key problem generation accuracy of a large model is improved.

Provided are a content generation method and apparatus, a model construction method and apparatus, and a data processing method and apparatus, a device, and a medium. The method comprises: in response to a dialogue initiated by a user, obtaining dialogue data (202); when this round of dialogue is the first round of dialogue, the dialogue data being a current question of the user in this round of dialogue; when this round of dialogue is not the first round of dialogue, the dialogue data comprising a current question of the user and previous historical questions of the user; inputting the dialogue data to an independent question transformation model, so that the independent question transformation model transforms the dialogue data into an independent question that represents a user intention of the user in this round of dialogue (204), wherein training samples used by the model comprise a plurality of rounds of dialogue samples and an independent question sample, and the independent question sample indicates a user intention of a latest round of dialogue sample among the plurality of rounds of dialogue samples; and inputting the transformed independent question to a multi-round dialogue system, so that the multi-round dialogue system generates reply content for this round of dialogue on the basis of the independent question (206). The reply accuracy of the multi-round dialogue system can be 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 discloses a large model retrieval enhancement generation method and device, equipment, a storage medium and a product, and relates to the technical field of question and answer large models.The method comprises the steps that questions input by a user are vectorized, retrieval is conducted in a preset vector database according to the vectorized questions, and a result set meeting preset conditions is obtained; and combining the vectorized question and result set into context information according to the prompt project template, and inputting the context information into a large language model for processing to generate a question answer. According to the method, retrieval is carried out according to the index tree of the vectorized question in the vector database, then the large model generates the answer according to the result set and the question, information in the long document is effectively integrated on different levels, different abstract levels are covered, the question can be understood and answered on different levels, and the user experience is improved. And the capability of processing complex queries is effectively improved.

The invention relates to an intelligent question-answering system and question-answering method for tobacco industry professional knowledge based on a big language, and the system comprises a training data collection unit which is used for collecting related professional knowledge and related work flow and system files of the tobacco industry, and carrying out the deep learning and training, and forming a knowledge base; the model construction unit is used for constructing a large language model platform; the user interaction unit is used for performing information interaction between employees and the constructed large language model platform; the text question and answer unit is provided with an answer text acquisition module, a question generation module and a preset dialogue generation model; the knowledge base access module is used for accessing the employee question and answer text as a knowledge base to the large language model platform through a preset dialogue generation model; and the matching answer output module is used for searching answers matched with the questions from the knowledge base and outputting the matched answers. Through combination with an existing business process, a large language model privatization deployment platform is constructed, internal enterprise WeChat is connected, and intelligent question answering and pushing are formed.

The present invention provides a system for processing user queries through an artificial intelligence (“AI”) pipeline, utilizing data chunking, question generation, and AI models to deliver contextually relevant responses. The system includes a server that ingests and chunks datasets, generates vector embeddings, and stores the data in one or more vector databases. A pipeline engine sends the chunked data to an AI model that generates potential questions tailored to different user personas. These questions, along with their corresponding data chunks, are stored in the database for future retrieval. When a user submits a query, the system semantically compares the query to the pre-generated question vectors and retrieves the most relevant question and associated data chunk. The query is then sent to an external AI model for final response generation. The system provides seamless interaction, delivering optimized, context-aware responses to user queries in real-time.

The invention relates to a large model SQL (Structured Query Language) generation method integrating few-sample prompt and a multi-choice mechanism. The method comprises the following steps: analyzing a query text input by a user, and determining database mode information corresponding to the query text; according to the query text and the database mode information corresponding to the query text, screening out similar samples from a preset sample library; inputting the query text, the database mode information corresponding to the query text and the similar samples into a pre-trained statement generation model to obtain candidate SQL statements; and selecting a target SQL statement from the candidate SQL statements according to the execution result of the candidate SQL statements. By the adoption of the method, the natural language input by the user can be analyzed, the corresponding database mode can be accurately obtained, the multiple candidate SQL statements are generated in combination with the corresponding similar samples input by the user, the target SQL statement matched with the input of the user is accurately screened out from the candidate SQL statements on the basis of the execution results of the candidate SQL statements, and the user experience is improved. And the accuracy of generating the SQL statement based on the natural language problem is improved.

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.

Computer-based techniques for performing systematic literature reviews are provided. Some embodiments include an SLR system including various components that interoperate to generate regulation-compliant SLR reports regarding an issue based on user-defined parameters. In several embodiments, the SLR system may integrate various artificial intelligence techniques to facilitate generation of SLR reports. For example, various SLR systems disclosed hereby may utilize generative pre-trained transformers to assist in aspects of systematic literature reviews, such as literature database queries, literature screening, data question generation, level of evidence classification, qualitative assessments, data extraction, and report generation.

The invention discloses a remote sensing question answering system based on a comparison-generation type pre-training model, which comprises the following steps of: firstly, collecting remote sensing images of tasks such as classification, semantic segmentation, target detection, image-text retrieval and image text generation as original data, and uniformly converting the original data into an image-text pair form; the category area proportion is calculated through segmentation processing, a target category and position information thereof are detected, and zero sample classification is performed through a CLIP model. Thirdly, inputting multi-source information into a multi-modal large language model, generating text description of an image according to a question, and completing construction of a remote sensing global visual question and answer data set; and meanwhile, based on the annotation box, cutting the picture and sending the picture into a multi-modal large language model to obtain a remote sensing local visual question and answer data set. Then, pre-training the comparison-generation type model from a coarse stage to a fine stage, so that the comparison-generation type model has global-local level remote sensing knowledge and visual question and answer ability; and finally, realizing question and answer interaction with the user by constructing a user-friendly system interface.

The invention discloses a cue word generation method and device of a large language model and electronic equipment, and relates to the technical field of information processing.The method comprises the steps that firstly, a malicious problem generation template data set is constructed based on a preset malicious problem data set, or constructing a cue word attack variant template data set based on a preset cue word attack template data set; then, the malicious problem generation template data set or the cue word attack variation template data set is input into the large language model, and a cue word data set is generated; through the method, the cue word can be automatically generated by using the large language model, diversified application scenes can be covered, the risk identification capability for different attack scenes is improved, and the problem of low efficiency caused by manual cue word writing is avoided.

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.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention provides an RAG question setting and automatic question judging system based on a large model, and belongs to the technical field of intelligent education of natural language processing. The problems that in the prior art, efficiency is low, accuracy is insufficient, and safety and expansibility are limited are solved. An overall framework is established based on RAG, and a large model local deployment and fine adjustment module, a teaching resource vectorization storage module and a large model question setting and judging module are arranged under the overall framework; the large model local deployment and fine tuning module is used for realizing data collection and preprocessing, model selection and loading, task fine tuning, model training and verification and localized deployment and reasoning optimization; the teaching resource vectorization storage module is used for realizing data uploading and storage, data vectorization processing and data auditing and monitoring; the large model question setting and judging module is used for realizing question generation and automatic question judgment; the method is applied to wisdom education.