11 results about "Problem set" patented technology

This invention belongs to the field of artificial intelligence technology. The embodiments of this invention provide a fully automated interview assistance method, system, and device based on a large language model. First, it acquires recruitment requirements, initial interview questions, and multimodal data of job seekers from the recruitment end to construct a multimodal dynamic knowledge base. This multimodal dynamic knowledge base includes content dynamic mechanisms, structure dynamic mechanisms, and iterative dynamic mechanisms, used to update the knowledge base content and sub-question sequences, update the structure of the knowledge graph in the multimodal dynamic knowledge base, and optimize sub-question generation and retrieval strategies, respectively. Then, based on the multimodal dynamic knowledge base, it determines the job seeker's structured question set, and subsequently determines the job seeker's interview assistance strategy. This invention, through three update mechanisms, makes knowledge application more closely aligned with actual interview scenarios, overcoming the shortcomings of traditional static and rigid knowledge bases.

The application relates to the technical field of retrieval enhancement generation evaluation, and provides a retrieval enhancement evaluation method and device based on multi-round dialogue context perception, which comprises the following steps: a first large language model is constructed; the first large language model is used for question rewriting according to a question and context; historical multi-round question and answer data of real users is extracted; a multi-round evaluation set is generated based on the historical multi-round question and answer data, a knowledge base document, a second large language model and a RAG system; the second large language model is used for summarizing a business classification, a question type and an interaction paradigm of each multi-round question and answer; the first large language model is used for converting the multi-round evaluation set into a single-round question set; and the RAG system is evaluated by using evaluation indexes, the multi-round evaluation set and the single-round question set. The application realizes automatic generation of the multi-round evaluation set, reduces the manual labeling cost, reduces the construction difficulty of the multi-round evaluation set, and realizes evaluation of the multi-round dialogue RAG task.

Methods and systems for generating a frequently asked questions are provided, which include defining, by a computer program executed by a computer, a first large language model (LLM) with a user query and a feedback of the user query; refining, by the computer program, the user query to a question set based on the feedback, the question set comprising one or more sentences; defining, by the computer program, a second LLM to generate a first set of question and answer pairs from a source document; defining, by the computer program, a third LLM to generate a content set from the source document based on a rewriting of the source document; selecting, by the computer program, top questions from the content set to be provided to the second LLM; and generating, by the second LLM, a second set of question and answer pairs based on the top questions.

The present application belongs to the technical field of teaching resource generation, and particularly relates to a method for automatically generating in-class questions based on text understanding of classroom lectures, which comprises the following steps: receiving multiple segments of externally input classroom lecture texts to form an initial lecture text corpus; instructing a large language model API through knowledge summary prompts to generate refined summary texts; taking the refined summary texts or the initial lecture text corpus as analysis objects to generate an ordered list of keywords; visualizing the ordered list of keywords and its associated quantitative data through a front-end interactive interface to determine target keywords for generating classroom questions; iteratively generating and self-verifying and correcting question-answer pairs to output final question-answer pairs; and uniformly presenting and outputting the final question-answer pair set, the refined summary texts and the target keyword list through the front-end interactive interface to form a set of in-class questions that can be directly used for classroom questioning or exercises. The present application effectively improves the efficiency and quality of classroom interaction.

This application provides a knowledge information retrieval method, apparatus, device, medium, and program product, which can be applied to the field of artificial intelligence technology, involving the application of large models in knowledge retrieval scenarios, and relating to the field of information security. The method includes: determining a target problem; determining N sets of correspondences between knowledge information and preset problems; wherein any knowledge information contains information for answering the corresponding preset problem; N is a positive integer; the preset problems are determined based on a set of candidate problems associated with the corresponding knowledge information, the set of candidate problems including: candidate problems determined based on similar knowledge information of the associated knowledge information, the information similarity between the similar knowledge information and the associated knowledge information being greater than a preset information similarity threshold; determining preset problems whose problem similarity to the target problem is greater than the preset problem similarity threshold, and determining the knowledge information corresponding to the determined preset problems as the basis information for answering the target problem.

The application provides a large model-based vertical field data construction method, and belongs to the technical fields of data processing and artificial intelligence, and comprises the following steps: converting a vertical field source document into an intermediate format text and dividing the intermediate format text into multiple text blocks; inputting the text blocks into a pre-trained generative language model, guiding the pre-trained generative language model to generate a plurality of candidate questions according to the contents of the text blocks according to a pre-designed prompt word, and performing preliminary screening and fine screening on each candidate question to obtain a question set; predefining a mode of a knowledge graph according to the field characteristics of the vertical field, processing all the text blocks based on an information extraction model, and constructing a field knowledge graph; and performing local context retrieval based on the text blocks of the source of each final question in the question set, performing global knowledge retrieval based on the field knowledge graph, and generating a final answer and a final thinking chain. The application is suitable for different vertical fields, and can effectively improve the quality of data and ensure the accuracy of question generation.

The embodiment of the application provides a kind of intelligent question and answer matching method, device, equipment, medium and product, it is related to natural language processing field, the method is generated by doing placeholder replacement processing to user question standardization question, can quickly peel off specific entity information, directly focus on the core semantics of question, reduce the search range from the source, avoid the inefficient operation of indiscriminate large-scale search comparison in mass data, simultaneously based on standardization question generation non-overlapping entity combination semantic variant problem set, can accurately cover the various expression forms of the core semantics of user question, reduce the invalid search caused by semantic expression difference, make subsequent search more targeted, then directly output the target query question with the highest matching degree by reordering semantic variant through semantic ordering model, can quickly lock the standard question most suitable for user question intention, solve the problem of low efficiency of question and answer matching in prior art.

This invention relates to the field of knowledge base construction technology, specifically to a question-and-answer knowledge base construction method, system, and storage medium. The invention categorizes question-and-answer pairs in the current batch to obtain question texts, their job tags, and business intents. It calculates the semantic repetition rate of any two question texts and groups the two question texts with the highest semantic repetition rate together. Through semantic repetition rate calculation and grouping, it achieves representative extraction of multiple question texts with different expressions but the same intent under the same job position, avoiding the duplicate storage of similar questions in the knowledge base. The invention constructs candidate question-and-answer pairs based on a representative question set, calculates a weighted matching rate using the storage duration of in-situ question-and-answer pairs in the knowledge base as a weight, and evaluates the degree of matching between candidate question-and-answer pairs and in-situ knowledge through the weighted matching rate.

The application discloses a question dynamic recommendation method, device and equipment based on a HITS algorithm. The method obtains a set of asked questions and a set of to-be-recommended questions; initializes hub degrees of the asked questions; initializes authority degrees of the to-be-recommended questions; for each to-be-recommended question, iteratively updates the authority degree of the to-be-recommended question based on an initial value of the authority degree of the to-be-recommended question and latest values of the hub degrees of the asked questions; for each asked question, iteratively updates the hub degree of the asked question based on an initial value of the hub degree of the asked question and latest values of the authority degrees of the to-be-recommended questions; and generates a question recommendation result according to the authority degrees of the to-be-recommended questions after the iteration is ended. The asked questions are abstracted as hub pages in the HITS algorithm, the to-be-recommended questions are abstracted as authority pages in the HITS algorithm, and the iteratively updated hub degrees and authority degrees realize dynamic recommendation of the questions.

The embodiment of the specification discloses a cross-language understanding method, device and equipment, relates to the technical field of artificial intelligence, and is used for solving the problem that the existing cross-language understanding is difficult to cope with high requirement scenes such as small languages and professional fields. The method comprises the following steps: determining a pre-constructed cross-language knowledge base; the construction of the cross-language knowledge base is: the source language original data of the current professional field is disassembled to obtain the source language question and answer pair in combination with external authoritative information; based on translation quality verification, the target language question and answer pair after translation of the source language question and answer pair is obtained to construct the cross-language knowledge base in combination with the source language question and answer pair; receiving a target language user question to be processed, disassembling the question, obtaining a sub-question set, and searching in the cross-language knowledge base to obtain the knowledge matched with the sub-question set in semantics; and generating a current target language answer based on the knowledge to complete the cross-language understanding.

This invention relates to an intelligent generation and error correction method and system for mathematical problem-solving steps based on artificial intelligence, belonging to the field of data processing technology. The method mainly includes: responding to a problem-solving request input by a user on a problem-solving interface; obtaining a structured problem set based on the target mathematical problem text in the problem-solving request; determining an initial problem-solving step sequence through the structured problem set; the initial problem-solving step sequence includes multiple problem-solving steps; correcting the initial problem-solving step sequence based on the structured problem set to obtain a corrected step sequence and its credibility score; if the credibility score is greater than a preset score, outputting the corrected step sequence corresponding to the target mathematical problem text on the problem-solving interface; if the credibility score is less than or equal to the preset score, inputting the corrected step sequence and the structured problem set into a mathematical problem-solving model to obtain an updated corrected step sequence, and using the updated corrected step sequence as the initial problem-solving step sequence.