50 results about "General-purpose language" patented technology

The invention relates to the technical field of language translation, in particular to a low-resource language translation method and system based on deconstruction distillation, and the method comprises the steps: obtaining parallel corpus data of a low-resource language and a general language; the method comprises the following steps: constructing a teacher model by taking parallel corpus data as input, constructing a student model trunk based on a pre-trained BERT big language model and optimizing the student model, calculating cross-task attention alignment loss based on the teacher model and the student model, and outputting and executing logits distillation based on the teacher model and the student model. Deployment of low-resource language translation is completed based on the trained student model, a high-quality knowledge migration source is provided for the student model by constructing the BERT teacher model subjected to full-parameter fine adjustment, and meanwhile, the problem of translation precision caused by insufficient low-resource language data is effectively solved by means of dual supervision of cross-task attention alignment and logits distillation.

The invention relates to a knowledge enhancement method and system based on a context structure. The method comprises the following steps: acquiring a query request input by a user; screening candidate evidences related to the query request from a pre-constructed case knowledge base based on multi-view retrieval; performing weighted calculation on a retrieval result of each view to generate an evidence score of each candidate evidence; screening based on the evidence scores to obtain a candidate evidence set and preprocessing the candidate evidence set; calculating the value and length of each candidate evidence in the pre-processed candidate evidence set; constructing an evidence selection problem based on a preset context window value and the value and length of each candidate evidence; solving the evidence selection problem to obtain an evidence subset with maximum value; generating a context structure based on the evidence subset with the maximum value and a preset prompt template; and inputting the context structure into a general language model for knowledge enhancement, and generating a knowledge enhancement result.

The invention discloses a multi-language PLC configuration software component library expansion method and system based on LLVM, and relates to the technical field of configuration software, the method comprises the following steps: obtaining language source codes of various types of programming languages supporting the LLVM front end; inputting the language source code of each type of programming language into a corresponding LLVM front-end compiler, and converting the language source code into a standard intermediate representation; establishing a general language characteristic analysis framework; establishing a general abstract metadata injection framework; building a mapping rule framework; integrating the general language framework, the general abstract metadata injection framework, the mapping rule framework and the output result, and generating a unified extended intermediate representation; and generating an interface component library conforming to the IEC standard. According to the method, any programming language with an LLVM front end is supported, a developer can directly utilize a mature algorithm library in each language ecosystem to select the programming language most suitable for a specific application scene, and the development period is remarkably shortened.

The invention discloses a vehicle-mounted scene hot word recognition method and related equipment, and relates to the technical field of Internet, and the method comprises the steps: obtaining voice data inputted by a user, extracting a hot word text from the voice data, and obtaining fusion features of the voice data and the hot word text; analyzing the hot word text to generate a plurality of mapping paths, and constructing a hot word language model based on the plurality of mapping paths; decoding the fusion feature based on the hot word language model to obtain a hot word decoding result; decoding the fusion feature based on the universal language model to obtain a universal decoding result; and determining a target recognition result based on the hot word decoding result and the general decoding result.

The invention provides a multi-model fusion vertical class domain knowledge question-answering method and system, and belongs to the field of artificial intelligence natural language processing. According to the scheme, a traditional single large model is disassembled into a plurality of specialized models for cooperative work; firstly, an enhanced small parameter model trained for the vertical field is used for performing document analysis, and fixed position information is extracted in combination with rules in an auxiliary manner; then performing intention understanding by using a general language model with relatively low parameters, assisted by a cue word strategy, a rule model or a knowledge graph; then, based on the analyzed document information and the understood user intention, knowledge retrieval and answer generation are carried out through a model with large parameters; and finally, carrying out style optimization by adopting a special small parameter model, and generating an answer conforming to the characteristics of the vertical class field. An answer backflow mechanism is further designed, and continuous optimization of the system is achieved. According to the scheme, the resource consumption is greatly reduced, the accuracy and efficiency of the knowledge questions and answers in the vertical class domain are improved, and the'illusion 'problem existing in the prior art is solved.

The embodiment of the invention provides a model construction method and device, a data risk identification method and device, a medium and a product. The model construction method comprises the following steps: acquiring a preset seed training data set, and training a preset offline intention recognition model by using the seed training data set; obtaining a to-be-marked training data set, and predicting a first intention label of to-be-marked training data in the to-be-marked training data set by using the trained offline intention recognition model; constructing prompt content containing to-be-marked training data and a plurality of preset intention tags, and inputting the prompt content to the universal language model, so that the universal language model identifies a second intention tag of the to-be-marked training data in the plurality of preset intention tags; and based on the to-be-marked training data with the same first intention label and second intention label, the preset online intention recognition model is trained, so that the manpower marking cost is reduced, and the recognition accuracy of the online intention recognition model is improved.

The invention belongs to the field of operation inspection intelligent models, and discloses a power equipment operation inspection intelligent agent construction method and a related system.The power equipment operation inspection intelligent agent construction method comprises the steps that a power equipment related professional word list is introduced when a corpus is constructed, and the power equipment related professional word list is combined with an original word list; the understanding accuracy of the model on electric power terminologies, equipment operation scenes and details in an operation and inspection process can be obviously improved; in the multi-stage progressive pre-training and subsequent fine tuning process, the pre-training model continuously learns in the corpus which is richer and more accordant with the power operation inspection requirement, and professional knowledge and general language understanding ability supplement each other. By adopting multi-stage progressive pre-training, the method can progressively transition from basic language ability to knowledge mastering of a power equipment scene, and the logical reasoning ability of the model in a complex scene is gradually enhanced.

A system may include a non-transitory storage medium storing computer program instructions and a processor configured to execute the computer program instructions to cause operations. The operations may include receiving, by a generic language model, a question from a user and querying, by the generic language model, a domain-specific knowledge model for an answer to the question. The operations may further include providing the answer to the user and / or providing documentation associated with the answer to the user.

The application discloses a kind of data knowledge extraction method, system, equipment and storage medium based on large language model, method includes: the original data obtained is preprocessed;Feature is extracted from original data, select corresponding task feature, remove the feature of high correlation by calculating the correlation coefficient matrix between features;Select the large language model of task, capture general language and knowledge, fine-tune the large language model on the labeled data set of specific field or task;Based on large language model inference and prompt word engineering, knowledge source information is extracted using a secondary block iteration method, and the context understanding ability of the large language model after fine-tuning is used to integrate the information extracted from different data sources;Define evaluation criteria, analyze the error cases of the large language model, identify the improvement direction, and adjust the model parameters and training data according to the evaluation results and error analysis.The application has the advantages of improving the efficiency and accuracy of the knowledge extraction method.

The specification discloses a patent retrieval method, device and equipment for multiple retrieval scenarios. Patent field language models are obtained by training general language models with fields of patent documents as training data. For multiple retrieval scenarios, positive and negative samples of each patent document are screened through different weight multi-path recall and random sampling, and training data of a patent semantic representation model of each retrieval scenario is constructed to train the patent field language models, and then the patent semantic representation model of each retrieval scenario is obtained to generate semantic vectors of each field of the patent, and the semantic vectors are stored in a patent retrieval vector database of each retrieval scenario. According to the semantic vectors of the retrieval fields, similar patent documents are searched from the patent retrieval vector database of the target retrieval scenario. Through the above scheme, the semantic representation ability of the patent semantic representation model for the patent fields of different retrieval scenarios is improved, and the retrieval precision is improved.

A small sample based general image counting method, comprising: performing feature extraction on a first image to obtain first features; performing attention calculation based on the first features, a general language representation and a general visual representation to obtain first example features of a first example target, wherein the general language representation is used to describe categories of different objects, the general visual representation is used to describe visual information of different objects, the first example target is an object related to an example frame in the first image, and the first example features include a first language representation and a first visual representation, the first language representation is used to describe a category of the first example target, and the first visual representation is used to describe visual information of the first example target; performing matching on the first example features and the first features to obtain a correlation feature map; and obtaining a first counting result related to the first example target based on the correlation feature map. The method can greatly improve the generalization of the counting algorithm.

The invention provides a large language model staged pre-training method and system. The method comprises the following steps: training a Transform model by using a basic data set, optimizing a negative logarithm likelihood target, and adopting an AdamW optimizer and a cosine attenuation learning rate; continuing training by using the universal knowledge data set based on the first-stage parameters; and weighting the professional data in the training field by adopting an oversampling strategy. By structuring a training target and a data type, the model can efficiently learn language basis, general knowledge and professional skills in stages. Experiments show that according to the method, the training efficiency of the model in the basic stage is improved by 40%, the overall training time is shortened by 30%, and meanwhile the accuracy rate of tasks in the professional field is 15%-20% higher than that of traditional end-to-end training. Finally, model parameters are evaluated through professional ability, and both universal language understanding and domain specialty are achieved.

The invention discloses an NL2SQL-based power intrinsic safety question-answering system construction method, system and device and a medium, and belongs to the technical field of power safety. The method comprises the following steps: collecting fine tuning data and constructing a fine tuning data set; performing low-rank adaptive fine tuning on a pre-trained large language model through the fine tuning data set to obtain a fine tuning large language model, and triggering an annealing strategy for optimization; building prompts through the fine-tuning large language model in combination with user questions and database structure information; and generating a structured query language statement based on the prompt, querying a database to obtain a query result, and adjusting the prompt according to the query result to process an error. According to the method, low-rank fine tuning in the field of electric power intrinsic safety is carried out on a general pre-training large model by introducing the LoRA technology, so that the model has general language understanding ability and industry exclusive knowledge at the same time, and the problem that a traditional model is high in universality but low in specialty is solved.

This application discloses a method and related equipment for identifying hot words in a vehicle-mounted scene, relating to the field of Internet technology. The method includes: acquiring user-input voice data; extracting hot word text from the voice data; and acquiring fusion features between the voice data and the hot word text; parsing the hot word text to generate multiple mapping paths; constructing a hot word language model based on the multiple mapping paths; decoding the fusion features based on the hot word language model to obtain a hot word decoding result; decoding the fusion features based on the general language model to obtain a general decoding result; and determining a target recognition result based on the hot word decoding result and the general decoding result.