92 results about "Open domain" patented technology

The invention discloses a dynamic intention recognition and GraphRAG fusion method and system for open domain questions and answers, and relates to the technical field of intelligent and natural language processing. The method comprises the following steps: constructing and dynamically updating a heterogeneous knowledge graph; performing dual-stage intention recognition on user query; subtasks are disassembled based on an intention result, and cooperative execution is carried out through multiple agents; service standardization integration is realized by means of a model context protocol; and closed-loop optimization is carried out in combination with user feedback. The system comprises an image knowledge base, an intention recognition module, an agent collaboration module, an MCP service integration module and a feedback optimization module. The system is a novel open domain question-answering system integrating structured knowledge modeling, dynamic intention understanding, intelligent collaborative reasoning and standardized service interaction, and through fusion of structured knowledge modeling and intelligent collaborative reasoning, question-answering accuracy and complex intention understanding ability are improved, system adaptability and efficiency are enhanced, multi-system collaboration is promoted, and the system has a wide application prospect. The method is suitable for scenes such as knowledge services and intelligent assistants.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention provides a knowledge distillation method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in particular to multi-modal artificial intelligence and data processing. The method comprises the steps that a candidate sample data set is screened out from an open domain data set based on a sample data set associated with a target industry and a feature coding layer of a teacher model after parameter fine adjustment, and the data size of the sample data set associated with the target industry is smaller than that of the open domain data set; on the basis of a student model and the teacher model after parameter fine adjustment, aiming at the distillation loss of each candidate sample data in the candidate sample data set, determining the learning value degree of each candidate sample data, and determining distillation training sample data on the basis of the learning value degree; and training a student model based on the distillation training sample data. According to the scheme, the reasoning calculation overhead in the data screening process can be reduced while the high-quality training sample set is rapidly constructed, and the knowledge distillation effect can be improved.

The invention provides a multi-agent mixed domain dialogue method which comprises the following steps: receiving a document and dialogue content input by a user, judging a dialogue mode to which the dialogue content input by the user belongs, and generating a processing plan to be executed according to the dialogue mode; executing the processing plan so as to call at least one large model agent, and receiving a dialogue feedback result generated by using a generation method corresponding to the dialogue mode when the plurality of large model agents input the optimization result of the dialogue content and the document; and summarizing the dialogue feedback result to generate a final result, and displaying the final result to the user. According to the method, a centralized multi-agent integrated framework can be utilized, a TOD agent and an ODD agent which solve specific problems can be combined, meanwhile, the improved retrieval enhancement generation method based on the knowledge graph is provided for open domain dialogues based on knowledge, and the problem of insufficient knowledge can be effectively solved.

The invention provides an open domain label system construction method and device based on a large language model, and relates to the technical field of data processing. The method comprises the following steps: constructing a label association pool according to different to-be-labeled contents in a first time period and initial labels output by labeling the to-be-labeled contents by using a first large language model; determining an initial tag system and a tag cluster of any initial tag based on the tag occurrence frequency of each initial tag; based on a preset semantic understanding task description, determining a normalized tag of each initial tag by using the zero sample capability of the second large language model; constructing a label directed graph set; and if it is detected that the first tag in the first tag directed graph is a normalized tag of the second tag in the directed graph and the normalized tag of the first tag in the second tag directed graph is a third tag, constructing a target tag system. According to the method, the time from emerging topic appearance to label system response is shortened, and the semantic accuracy of the label system is improved.

The invention discloses a self-adaptive public opinion data classification method and system for an open domain, and the method comprises the steps: collecting original text data from a plurality of data sources, carrying out the preprocessing of the original text data, obtaining a plain text list, converting the plain text list into high-dimensional semantic vectors in batches, and enabling all high-dimensional semantic vectors to form an embedded matrix; calculating a minimum clustering number and a maximum clustering number according to the number of texts in the plain text list, generating various clustering schemes corresponding to the embedded matrix through all clustering thresholds in a clustering range, calculating a comprehensive score of each clustering scheme, and selecting the clustering scheme with the highest comprehensive score as an optimal scheme; generating subject terms of all clustering clusters based on a large model; and integrating the optimal parameters, the texts in each cluster and the subject terms in each cluster, and outputting a structured classification result. The open domain public opinion data can be efficiently, intelligently and interpretably classified.

The invention discloses a customized image generation method and device based on main body consistency, equipment and a storage medium, and the method comprises the steps: screening images and texts from a public data set, and constructing an open domain image and text data set; generating a target map by means of an existing model, and constructing a good and bad preference comparison data set; extracting an image by using a pre-trained coding model, and embedding and associatively storing the image and the text to establish a multi-modal feature library; fusing and embedding through an attention mechanism to generate a multi-modal fusion feature; diffusion and noise addition are carried out on the good and bad target images to serve as training supervision signals; and generating a subject consistent target map by using a noise distribution training model. According to the method, through signal supervision of subject consistency provided by a target, model learning is enabled to balance subject reservation and instruction following, test fine tuning is not needed, generalization and generation efficiency is improved, the problems of cooperative control and insufficient generalization efficiency of an existing method are solved, and the method can be widely applied to customized and personalized image generation scenes.

The invention relates to the technical field of machine learning, in particular to a defense method and device for graph neural network backdoor attacks, equipment and a medium, when an open domain node classification task is received, the open domain node classification task is input into a preset trigger detection model, the model can determine unknown class nodes and cut edges of the unknown class nodes to obtain an initial defense sub-graph, and the initial defense sub-graph is used for defending the open domain node classification task. And performing importance score calculation on the target defense nodes in the initial defense subgraph to form a final defense subgraph, inputting the final defense subgraph into a preset dynamic classifier, and outputting a classification result of the target defense nodes. According to the method, the backdoor attack problem faced by the graph neural network in an open domain scene is effectively solved, and the classification accuracy and security are improved.

The invention relates to the technical field of natural language processing, and discloses a knowledge injection and graph neural network-based open domain text information extraction method, which comprises the following steps of: extracting all noun phrases from input text data to construct a candidate entity set; combining the candidate entities in pairs, and constructing a self-attention incidence matrix of each entity pair; performing sequence sampling on the self-attention incidence matrix to generate a candidate triple sequence set; calculating semantic similarity between the candidate triple sequence and the input text data, and outputting the first k high-correlation triple sequences as initial information extraction results of the input text data; and performing dependency structure analysis on the initial information extraction result based on a graph neural network, and generating a triple sequence through redundant sequence labeling as a final information extraction result. According to the method, the recognition rate of the complex syntactic structure triad in the open domain information extraction task is remarkably improved, and meanwhile, the redundancy of the extraction result is effectively reduced.

The invention provides a large language model retrieval enhancement generation method based on adaptive rewriting selection, and is suitable for the field of natural language processing and information retrieval. According to the method, a pre-trained large language model is introduced to automatically generate diversified rewriting queries, and a self-supervised rewriting sequencer is combined to perform correlation evaluation and sequencing on candidate rewriting statements. Through a context multi-arm bandit selector, the optimal rewriting number is dynamically determined according to query semantics, a high-quality rewriting subset is selected in a self-adaptive mode, and the coverage degree and precision of information retrieval are effectively improved. And the rewriting-driven knowledge retrieval module utilizes a plurality of high-quality rewriting, integration and deduplication related knowledge blocks in parallel, and continuously optimizes a Bandit strategy based on a feedback signal to realize online self-learning. Different from a traditional RAG system depending on fixed parameters and static rewriting, the method can intelligently adjust the retrieval process for complex or variable queries, and the accuracy and practicability of a retrieval enhancement generation system in an open domain and a multi-hop reasoning scene are remarkably improved. According to the method, efficient and flexible technical support is provided for intelligent question answering and knowledge discovery in a complex environment, and the application effect and popularization value of the large language model are greatly enhanced.

Systems and methods for open domain dialog segmentation and state tracking are provided. Specifically, a computing device may acquire and analyze a dialog in near real-time, generate a structured cue template for a state prediction model based on the dialog, and generate a structured output using the state prediction model based on the structured cue template. The structured output includes a round summary and a state tag for each round of conversation.

The invention belongs to the technical field of digital humans, and relates to a large-model-driven real-time interaction digital human driving method, system and device and a storage medium, and the method comprises the steps: 1) obtaining the dialogue input of a user, and inserting a dialogue background and a historical question and answer pair for the dialogue input to form an input text of a large language model; 2) inputting the input text of the large language model into the large language model, and generating answer content by the large language model; 3) generating voice data based on the answer content; 4) generating facial animation and limb animation data based on the voice data; 5) playing the voice data, and driving the digital person by using the facial animation data and the limb animation data; and 6) outputting the real-time dialogue content of the digital person in the video stream. According to the method, the problems of insufficient semantic level synchronization precision and low interaction naturalness during digital human face action driving are solved, open domain real-time question answering is supported, and the natural performance of digital human real-time interaction is realized.

The invention provides a teacher-query guide type compression optimization training method and system based on open domain questions and answers and a query method and system. The compression optimization training method comprises the steps that candidate paragraphs are obtained according to user queries, each query is matched with the corresponding candidate paragraphs, the candidate paragraphs are segmented into sentence sets, and training units are formed; the teacher model performs data distillation on the training unit based on a preset requirement to generate a distillation sample set; the method comprises the following steps: respectively coding queries and sentences in a distillation sample set into vector representations through a predefined coding function, measuring the similarity of the queries and sentences, and training a student model by taking the similarity as a supervision signal to obtain a trained student model; through a teacher model knowledge migration and query focusing mechanism, the accuracy and efficiency of a student model in an extraction type compression task are improved, redundant information interference is reduced, more compact and highly related context input is provided for a retrieval enhancement generation framework, and the method is suitable for an efficiency-sensitive open domain question and answer scene.

The invention relates to the technical field of natural language processing and artificial intelligence, in particular to an open domain question and answer task processing method and a comprehensive processing framework system. A complete, reliable and explainable reasoning chain from an original question to an explicit reasoning basis and then to a final answer is constructed. According to the framework, the accuracy and robustness of a question answering system are remarkably improved, and the illusion problem of a large language model in an open domain scene is effectively relieved. According to the method, through three innovation mechanisms of question enhancement, collaborative evidence collection and multi-stage abstract integration, substantial technical progress is made in the aspects of accuracy, robustness, interpretability, information integration capability and the like, and an effective solution is provided for constructing a high-reliability large language model open domain question-answering system.

The application discloses a planning calibration method and system for an information analysis intelligent assistant, and comprises the following steps: in each planning round, a candidate strategy set is generated by a planning agent, the candidate strategy set contains an information retrieval target, a retrieval keyword combination and an information processing flow; the candidate strategy set is evaluated by multiple agents with heterogeneous private memories, a strategy selection module based on the agents is constructed for information consistency, and a strategy with the most stable evaluation result is selected as an optimal strategy of the round; a difference between a self-selected strategy of the planning agent and the selected optimal strategy of the round is recorded, a calibration constraint module guided by consistency is constructed, and the difference is converted into a cognitive calibration constraint and stored in the memory of the planning agent; strategy selection optimization is realized in single-round planning by relying on the strategy selection module based on information consistency; and the cognitive calibration constraint generated by the calibration constraint module guided by consistency is combined, so that the constraint is transmitted between different rounds and gradually accumulated, and a constraint is formed on strategy generation of the planning agent; and the trained agent is applied to an open domain webpage retrieval task to generate accurate retrieval results.

The application discloses a training method and device of a question and answer model, equipment and a storage medium. When training the question and answer model, an initial question and answer model including an initial retrieval module and an initial reading module is constructed. Training corpus including a first target document is obtained, and the initial retrieval module and the initial reading module are jointly trained according to the first target document. In the joint training process, the initial retrieval module obtained after the i-th iteration training is used to update the first target document used in the i-th iteration training, so that the initial retrieval module and the initial reading module obtained after the i-th iteration training are respectively subjected to (i+1)-th iteration training according to the updated first target document, until the iteration training condition is met. According to the model parameters when the iteration training condition is met and the network structure of the initial question and answer model, a target question and answer model is determined. In this way, the two modules promote each other, significantly improve the effect of the question and answer model, and improve the accuracy of open domain question and answer.

The application discloses an open domain sentiment image editing method and system based on multi-agent cooperation, and the method steps comprise the following steps: in step S01, a to-be-edited image, a target sentiment category and an editing intensity are acquired and input to a planning agent; in step S02, the planning agent analyzes all visible elements in the image and performs semantic hierarchical classification, selects a dominant strategy and a target sub-style, generates a negative constraint condition, and finally generates an editing plan; in step S03, a plan verification agent audits the feasibility of the editing plan generated by the planning agent; in step S04, an image editing agent receives the editing plan that passes the audit, and generates a candidate edited image; and in step S05, a result verification agent evaluates whether the candidate edited image meets preset conditions. The application can dynamically find an open domain editing strategy that meets a target sentiment, and improves context adaptability and robustness when facing real and complex images.

The application discloses a multi-modal document view-based intelligent question answering system and method, and relates to the field of natural language processing. In order to solve the problems that a traditional open domain question answering system is complex in layout for different types of document views and it is difficult to uniformly model all objects in the prior art, the technical scheme provided by the application is as follows: a multi-modal document view-based intelligent question answering system, which comprises a multi-modal document view analysis module, a multi-modal document view retrieval module and a multi-modal document view question answering module; the multi-modal document view analysis module is used for extracting text information; the multi-modal document view retrieval module is used for retrieving document views related to preset information in the text and performing priority arrangement; and the multi-modal document view question answering module is used for extracting the document views with higher priority and outputting. The application is suitable for application in intelligent question answering of multi-modal document views.

The present invention discloses a method for constructing a cultural knowledge graph based on a W2ner model, which belongs to the field of culture and artificial intelligence technology, comprising S1: data collection; S2: complex entity recognition of the collected data based on the W2ner model; S3: open domain relationship extraction based on a large language model; S4: using a K-means clustering algorithm to effectively merge relational phrases and perform relationship normalization; S5: storing the normalized knowledge triples obtained after identification, extraction and alignment in a database, and performing visual output; the present invention integrates specialized technical modules for complex entity recognition, open domain relationship extraction and relationship normalization, and combines them with graph database storage to form a knowledge graph construction technical solution that is more adaptable to cultural text characteristics and has a higher degree of automation.

A first query having a first privacy status is received. A response to the first query is obtained based on output(s) of one or more machine learning (ML) models of an open domain dialog system. Each ML model is trained to predict responses to queries having the first privacy status. Data associated with the first query and the response is provided as training data for the ML models, in view of the first privacy status. A second query having a second privacy status is received. A closed domain dialog system associated with a context of the second query and having a privacy status corresponding to the second privacy status is identified. The second query is forwarded to the closed domain dialog system for obtaining a response to the second query. Data associated with the second query is not provided to train the ML models of the open domain dialog system.

The invention provides an open domain information extraction algorithm based on a graph neural network and relation discrimination. The method comprises the following steps of: extracting an entity relationship in a text and finishing construction of an entity relationship graph; performing text embedding representation and context coding on the input text; fusing the entity relationship to perform graph information embedding; and decoding is completed by using a double affine network, and an SPO triple extraction result is obtained. The invention aims to better solve the problem of open domain information extraction. According to the algorithm, technologies such as a pre-training model and a graph neural network are combined, meanwhile, the thought of relation judgment is combined, entity relation information is embedded into the network in a topological graph structure, and therefore the model can better utilize dependency knowledge in a text. Therefore, the problem that a traditional open domain information extraction method is limited in effect when facing complex texts and the defect that text dependent information cannot be fully utilized are overcome. According to the method, the open domain information extraction requirement in a complex scene can be met, and a technical basis can be provided for downstream tasks such as a question answering system and knowledge graph construction.

The present invention relates to a method and system for generating difference-aware replies based on remote supervision and knowledge distillation. The method comprises the following steps: Step A: Collecting natural, open-domain conversations on a social platform, using a retrieval system to retrieve background knowledge related to the conversations from Wikipedia, and labeling real replies to construct a training dataset DE; Step B: Using the training dataset DE, training a deep learning network model M based on remote supervision and knowledge distillation. This model selects the required background knowledge and generates replies based on the selected background knowledge; Step C: Inputting the conversation context and background knowledge base into the trained deep learning network model M to generate corresponding replies. This method and system are beneficial for improving the accuracy of generated replies.

The application discloses an open field large model reasoning enhancement method based on probability process supervision. The application adopts reinforcement learning to perform multi-round iterative optimization on a strategy model: in each round, a dynamic golden thinking chain is generated based on a question and an answer as a reference, a plurality of exploratory paths are sampled, a step-level path loyalty reward is calculated through alignment to quantify logical effectiveness, a layered reward mechanism is constructed by combining a weighted process reward and answer correctness, and a final reward signal is used to update the model. The method does not require an external reward model and artificial labeling, realizes fine-grained process supervision through self-supervision, dynamically updates the supervision signal to adaptively improve the model, is highly versatile, and is suitable for various open reasoning tasks.

The application provides a medical text classification method and device based on prompt learning. The method comprises the following steps: obtaining prompt information for primary classification from original medical text based on event prior information and knowledge prior information, wherein the primary classification comprises a department category; filtering the original medical text, and inputting the filtered text and the prompt information into a large language generation model after integration; calculating the similarity between the result sequence output by the large language generation model and each secondary classification label representing a disease category under the primary classification, and taking the label category corresponding to the maximum value of the similarity as the secondary category output by the large language generation model. The application can realize primary classification based on the department category, and can also realize secondary classification based on the disease category under the primary classification, which is more consistent with the general cognition in the medical field, so that the classification result is more standardized; meanwhile, the classification label can be unfixed, so that multi-level text classification in an open domain can be effectively realized.

The application discloses a retrieval enhancement generation method and system based on knowledge boundary perception, an electronic device and a storage medium, and belongs to the technical field of natural language processing. The method comprises the following steps: generating a high-quality supervised track by using a teacher model, and learning the ability of gap planning and answers by instruction fine-tuning of a weak model; paired samples reflecting overconfidence and over-conservatism are constructed, and a DPO algorithm is used for confidence calibration; gap planning is generated by a student model during actual prediction, and it is accurately determined whether each knowledge point needs retrieval according to cognitive information labels, and accurate retrieval is triggered only for the knowledge points with knowledge gaps. The application can be widely applied to open domain question answering, dialogue systems and knowledge-intensive tasks by explicitly identifying knowledge boundaries, dynamically adjusting thresholds and fine-grained on-demand retrieval, while ensuring the accuracy of answers, significantly reducing the consumption of computing resources and response delay.

This invention discloses a reliable reasoning and question-answering method and apparatus based on the collaboration of a large model and a knowledge graph, belonging to the fields of artificial intelligence and natural language processing. It extracts keywords through semantic guidance and links them to entities in the knowledge graph to determine the initial entities; then, it explores paths based on a decision-evaluation collaborative mechanism, expanding paths by combining hard constraints and semantic soft guidance, and dynamically pruning based on the difference in immediate rewards and patience thresholds; finally, it outputs the answer and a traceable reasoning path. This invention solves the problems of rule-dependent entity recognition, black-box reasoning process, and poor interpretability in open-domain question answering, balancing semantic understanding generalization ability with controllable and reliable reasoning process, improving question-answering accuracy, reasoning efficiency, and result reliability, and its overall performance is superior to traditional rule-based, heuristic, or pure neural network-based methods.

The application provides a large language model driven open domain natural language reasoning question and answer system and method, a question rewriting module rewrites a user question into a rewritten question; a center calculation and management module manages calculation and knowledge resources of a large language model, and outputs the calculation and knowledge resources of the large language model required by a question core engine module to one or more sub-question and answer modules in a question and answer core engine module according to the type of the rewritten question; the question and answer core engine module reasons to obtain one or more candidate answers of the rewritten question and explainability information of the candidate answers according to the rewritten question and the calculation and knowledge resources of the large language model; and an aggregation reasoning module aggregates and reasons to obtain a final answer of the rewritten question and explainability information of the final answer according to the one or more candidate answers of the rewritten question and the explainability information of the candidate answers, and supports comprehensive question types, is easy to expand, is explainable, and has strong universality by using a large language model.

An open domain corpus relationship joint extraction method comprises the following steps: S1, extracting the feature vector of characters in the corpus; S2, performing feature fusion in the graph attention network; S3, extracting the relationship phrase in the corpus; S4, extracting the entity pair phrase in the corpus; S5, according to the relationship phrase extracted in step S3 and the corresponding entity pair phrase extracted in step S4, the three tuples are formed, and the confidence of the three tuples is determined, if the confidence is greater than or equal to the set confidence threshold, then the three tuples are taken as the open domain relationship three tuples of the input corpus. Through the above scheme, the problems of redundant relationship three tuple sequence, overlapping relationship three tuple, and low relationship three tuple extraction accuracy in open domain relationship extraction are solved.

The invention provides an insurance intelligent customer service method and device based on a large language model. The device comprises an insurance knowledge base module, a data access module, a data processing module, an insurance knowledge graph module, a multi-modal fusion module, a large language model module and a response generation module. By introducing an optimized GPT-NEOX large language model and fusing an InsuranceQA professional corpus and an InsurKG insurance knowledge graph, diversified insurance problems proposed by users can be dynamically understood, and natural, accurate and interpretable answers are generated. Compared with a traditional static answer extraction mode based on BERT and other models, the method has the remarkable advantages in the aspects of context understanding, open domain question and answer adaptability, deployment efficiency and professional domain knowledge coverage, and the response quality and user experience of insurance intelligent customer service are effectively improved.

The application relates to an open domain long text classification method and device based on theme analysis. The method comprises the following steps: constructing a field self-adaptive word table; preprocessing original long text to obtain purified text; using an LDA model to construct a latent theme space; extracting a core sentence group from the long text through semantic clustering to generate an abstract; mapping the abstract to the theme space to calculate a correlation degree score and output a theme identification; and converting the theme identification into a business classification label by querying a theme-label mapping table. The application effectively solves the problems of complex long text semantics and dynamic label system changes, and improves classification accuracy and expansibility.