58 results about "Input language" patented technology

The embodiment of the invention provides a multi-modal fusion entity retrieval enhancement generation method and device, and the method comprises the steps: carrying out the blocking and adaptive text extraction of multi-modal data in an offline stage, obtaining the text block data corresponding to each modal data, extracting the entity and relation of the text block data according to a language model, constructing an entity triple, and carrying out the segmentation and adaptive text extraction of the entity triple. Fusing the entity triad with the text block data to obtain an offline knowledge graph, and constructing a data index; in the present stage, a query statement of a user is received, text block data most similar to the query statement are retrieved in a knowledge graph through a data index, after entity aggregation is carried out on the retrieved text block data, the text block data are reordered according to retrieval scores, an entity aggregation result is obtained, entity ordering is carried out according to the entity aggregation result, and the entity aggregation result is obtained. By means of the multi-modal retrieval method and device, the efficiency and accuracy of multi-modal retrieval can be improved.

The invention discloses an active optical alignment method based on a local lightweight language model, and particularly relates to the technical field of precision installation and adjustment, comprising the following steps: extracting image indexes and platform attitude information through image acquisition, constructing a unified input format, verifying and inputting the language model; generating a six-degree-of-freedom pose adjustment strategy and a credibility score; the strategy is executed by the platform control device after being verified by a preset criterion; scoring is performed again after execution, and whether a feature posture is recorded or error compensation is triggered is judged according to a scoring result, so that subsequent input and strategy generation are optimized, and alignment precision and stability are improved; according to the method, the data consistency is guaranteed through a unified input format and a verification mechanism, the alignment precision is improved in combination with a dynamic scoring strategy, and the stability and fault tolerance of the system are enhanced by introducing a feature attitude recording and error back-filling mechanism, so that a strategy generation and execution process with high reliability, high adaptation and high robustness in an active optical alignment task is realized.

The invention discloses a feature fusion optimization-based multi-modal map enhancement retrieval method and a dialogue system. The method comprises the following steps of: respectively carrying out pre-training and fine tuning on a visual model and a language model by utilizing a domain image and text data; constructing a knowledge graph based on the text data in the knowledge base and constructing a vector database containing associated image data; performing semantic analysis and optimization on the original query of the user by using the language model and forming a structured retrieval intention; searching related sub-graphs, text semantic vector information and associated image data based on the search intention; encoding the sub-images into knowledge contexts, inputting the knowledge contexts into a dynamic prompt generator to generate visual prompts, and extracting enhanced visual features from the associated image data through a visual model; and inputting the subgraph, the text semantic vector information and the enhanced visual features into a language model for collaborative reasoning, and generating and outputting a final answer. According to the method, deep fusion and accurate retrieval of multi-modal knowledge can be realized, and the accuracy and efficiency are remarkably improved.

To provide an information processor for supporting the generation of a sentence using a language model.SOLUTION: The above problem is solved by an information processing device including chatbot control means for asking one or more questions to an end user according to a scenario and acquiring an answer to the question, prompt generation means for generating a prompt in which one or more variable portions included in a template prompt are replaced with an answer corresponding to the variable portion, and sentence data acquisition means for acquiring sentence data generated by inputting the generated prompt to a language model, in which the chatbot control means displays the acquired sentence data on a user terminal operated by the end user.SELECTED DRAWING: Figure 3

Described herein is a language-based camera parameter generation system that sets the parameters for the ISP and / or control of a digital camera from a user-input language prompt, such that the capture and processing of the ISP matches the visual quality described by the language prompt. The camera operator provides a language-based description, such as a short sentence (for example, “dreamy and awe-inspiring image that is well exposed”) before taking a photo, and the system will generate the control and ISP parameters such that captured image or video will have visual qualities that match the language prompt. This gives a new way for the camera user to control the visual quality of the image and enables new creative expressions. The benefit of a language-based approach is that it is more natural and intuitive than manually setting numerical values.

The invention provides a training sample generation and model training method and device, a storage medium and a product. The training sample generation method comprises the steps of obtaining original dialogue materials of a target application scene, wherein the original dialogue materials comprise a model input question and a model output reply; performing named entity recognition on the model output reply, and extracting N target entities, N being greater than 0; on the basis of the N target entities and the incidence relation between the different target entities, the original dialogue material is disassembled into a plurality of question and answer pairs, so that each question and answer pair corresponds to one target entity or a group of target entities with the incidence relation; the multiple question and answer pairs are input into a language model, so that the language model conducts logic consistency detection on all the question and answer pairs and outputs corresponding detection results, and the detection results comprise inconsistent conclusions and combination of magic view types or consistent conclusions; and based on each question and answer pair and the corresponding detection result, generating a first training sample used for training the magic vision detection model.

The embodiment of the invention provides a data processing method and a legal problem processing method.The data processing method comprises the steps that a problem to be processed is determined, and the problem to be processed is input into a language generation model; in the language generation model, reference data corresponding to the to-be-processed question is obtained from the question and answer retrieval database, the question answer of the to-be-processed question is generated, the reference data is any target question and answer pair stored in the question and answer retrieval database, and the reference data is any target question and answer pair stored in the question and answer retrieval database. The target question and answer pair is obtained by performing text processing on a plurality of different types of initial text data, the plurality of different types of initial text data are determined from a plurality of data sources, and the plurality of different types of initial text data correspond to the same target field.

A test case generation method. The method comprises: acquiring a structured rule for a first application programming interface (API) and a natural language description for the first API; querying a domain-specific language (DSL) database on the basis of the natural language description for the first API to obtain a query result, wherein the query result comprises at least one of: a DSL sample where a natural language description part matches the natural language description for the first API, a DSL error correction example, or a DSL syntax; constructing a prompt on the basis of the query result, the natural language description and the structured rule; and inputting the prompt into a language model to generate a target DSL code, wherein the target DSL code is used for testing the first API, or is used for generating a test case for performing integration testing on the first API and a second API. In the method, a DSL sample, an error correction example, and a syntax are dynamically matched from a DSL database to generate a highly controllable and structured context, and a test case is generated by means of language model inference on the basis of the context, thereby integrating unit testing and integration testing, and reducing repeated development.

The present disclosure relates to a model prompt generation method and device, electronic equipment and computer readable medium, belonging to the field of artificial intelligence. The method comprises: obtaining a corresponding input question vector according to an input question text; matching the input question vector with text segment vectors in a vector database to determine a plurality of candidate text segment vectors; the text segment vectors in the vector database include a plurality of parent segment vectors, and child segment vectors in each parent segment vector; converting the candidate text segment vectors into corresponding candidate text segments, and filtering and processing according to parent-child relationship data between each candidate text segment to obtain a target text segment; obtaining a language model prompt used in a language model according to the input question text and the target text prompt. The present disclosure can make the content of the language model prompt more reasonable and sufficient, thereby improving the correct rate of the language model in the private knowledge field.

The system appropriately generates output language signals that represent linguistic information related to the vehicle's operation. [Solution] The signal generation system comprises a first arithmetic unit configured to generate a route signal representing the planned route of a vehicle based on sensor signals output from sensors mounted on the vehicle, and a second arithmetic unit configured to generate an output language signal representing language information to be provided to the occupants by inputting an input language signal representing the content of speech by the vehicle occupants in natural language form into a trained language model. At least one of the first and second arithmetic units has a conversion unit that converts at least one of the sensor signal and the signal generated based on the sensor signal into a route data signal expressed in a format that can be input into the trained language model. The second arithmetic unit generates a route language signal relating to the planned route as an output language signal based on the route data signal.

The invention discloses an electric power system risk management and control method, device and system and a storage medium. The method comprises the following steps: receiving inquiry data which is input by a user and is related to electric power system risk management and control; inputting the inquiry data into a language model for semantic analysis to obtain a semantic vector, and inputting the semantic vector into a classification model to determine a target intention category of the user; extracting scene keywords related to a power grid system from the target intention category, mapping the scene keywords into a preset professional knowledge base to obtain retrieval fields, performing retrieval in the professional knowledge base based on the retrieval fields to obtain risk knowledge fragments related to risk management and control, and performing risk management and control on the risk knowledge fragments. Filling the risk knowledge fragment into a preset answer template to generate a risk management and control scheme; and managing and controlling the risk prevention and control process of the power system based on the risk management and control scheme. The accuracy of risk management and control of the power system can be improved based on the professional knowledge base.

A training data generation method for training an artificial neural network model including inputting a first prompt related to at least one first object in a specific context into a language model, acquiring first text data related to the at least one first object output from the language model, acquiring a first image related to the specific context, generating, based on at least one of the first text data or the first image, arrangement information related to an arrangement of the at least one first object for the first image, generating, based on at least one of the first text data, the first image, or the arrangement information, a second image in which the at least one first object is arranged in the first image, and outputting the second image.

A method including advancing a computer-automated workflow a step. The method also includes retrieving an image template including a computer renderable data structure for rendering an image. The image template is specific to the step. The method also includes retrieving a prompt template including a prompt data structure for input to a language model. The prompt template is specific to the step. The method also includes combining the image template and the prompt template to generate a combined prompt. The method also includes executing a language model with the combined prompt to generate an output of the language model. The method also includes rendering the output to generate a rendered data structure.

The embodiments of this application relate to the field of computer model-based image processing, and particularly to a system and method for processing brain images. The system for processing brain images provided by the embodiments of this application utilizes an encoder and a large language model to encode electromagnetic signals acquired by the signal acquisition unit into words and perform deep semantic processing on these words. This allows the image decoder to process the processed words and obtain an image. The encoder, the large language model, and the image decoder work together to transform electromagnetic signals into accurate brain images. The method for processing brain images provided by the embodiments of this application compresses electromagnetic signals and extracts effective information to obtain words, ensuring signal authenticity and reducing data volume. By inputting the words into a large language model for processing, the brain image obtained after decoding the processed words is more accurate.

The application provides a fault positioning method and device, electronic equipment and storage medium. The method comprises: acquiring a plurality of sensor parameters and alarm information of a target device; determining abnormal sensor information in the plurality of sensor parameters; performing splicing processing on the abnormal sensor information and the alarm information to obtain an alarm statement; inputting the alarm statement into a language model to obtain fault component information of the target device, wherein the language model is obtained by training alarm statements and corresponding component information. The language model in the above scheme can determine fault component information according to sensor parameters and alarm information of a target device, and the efficiency of positioning fault components is higher than relying on artificial experience.

The invention discloses a wind turbine blade defect small sample classification method based on a double-language prompt optimization adapter, and the method aims at the types of cracks, erosion, peeling, caving, normality and the like, and achieves the precise recognition under the condition of extremely few labeled samples. According to the method, a double-language text encoding module comprising a CLIP English text encoder and a Chinese RoBERTa encoder is constructed, and Chinese features are aligned to a CLIP feature space through a lightweight projection network to realize cross-language consistent representation. For the difficult categories, a category specificity prompt optimization process is provided, the difficult categories are screened based on initial evaluation, a multi-semantic-dimension candidate template is constructed, and a positive contribution prompt set is formed by contribution degree filtering of a leave-one-out method, so that the difficult category distinguishing ability is improved. Further generating unified category text features by adopting a double-language feature fusion strategy (gating self-adaption or fixed weight fusion), and combining a TIP-Adapter cache mechanism to complete training of small-sample-free adaptation by performing hyper-parameter and weighted fusion on zero-sample similarity output and cache matching output, so as to obtain a training result; meanwhile, a language perception strategy is introduced to automatically adjust Chinese and English branch weights according to an input language so as to give consideration to efficiency and stability.

A method includes inputting an input text sequence into a language model to generate a plurality of speech samples; applying one or more preference models to the plurality of speech samples to generate a set of preferred samples and a set of non-preferred samples; applying direct performance improvement optimization to preference data associated with the set of preferred samples and the set of non-preferred samples to generate optimized samples; and training the language model based on the optimized samples.

The invention discloses an RAG system operation method and system based on attribute-based encryption and user behavior perception, mainly relates to the technical field of RAG systems, and is used for solving the problems that in an existing scheme, a defense mechanism lacks dynamic adaptability, privacy protection and content quality are difficult to balance, and audit analysis dimensions are single. Comprising the steps of determining a text block to be decrypted and a decryption strategy based on a current user access strategy, source credibility and consistency of the text block and a preset sensitivity level; inputting the decrypted text blocks and user retrieval requirements into a language model to generate candidate answers; when it is identified that the candidate answer contains a preset sensitive entity, determining a privacy budget allocation value corresponding to the preset sensitive entity according to a preset sensitive level of the preset sensitive entity; and performing noise injection processing on the preset sensitive entity by using a logarithmic probability disturbance noise injection algorithm and the privacy budget allocation value to obtain a replacement lexical element of the preset sensitive entity in the candidate answer.

The invention provides a label generation method. The method comprises the steps of obtaining first content and supplementary data; determining a second content related to the first content from the supplementary data; determining a first label related to the first content and the second content from the label set; and inputting the first content, the second content and the first tag into a language model to obtain a second tag. According to the label generation method, the label related to the content can be screened out from the label set through the language model under the condition that the label recall rate and the accuracy rate are both considered.

This specification provides a risk control rule mining method, device, storage medium, and program product. The risk control rule mining method includes: acquiring a sample set containing multiple samples, each sample including the value of at least one risk feature variable and associated risk labels; binning each risk feature variable according to binning parameters and the value of the risk feature variable in the sample set, and performing statistical analysis based on the distribution of risk labels associated with samples in each bin to obtain binning feature information of the current binning method; constructing a first prompt word based on preset binning decision knowledge, semantic information of risk feature variables, and binning feature information; inputting the first prompt word into a language model to enable the language model to perform binning rationality analysis, outputting binning parameters for the next binning process when the current binning method is determined to be unreasonable, or outputting risk control rules for the risk feature variables when the current binning method is determined to be reasonable.

To provide a program or the like for inputting a video expression capable of understanding a long-term action to a language model.SOLUTION: Time-series frame conversion means for converting a video into a plurality of time-series frames in order to input an embedding representation based on the video to a language model, spatial frame conversion means for converting the plurality of time-series frames into a spatial frame arranged on a grid-like plane, a first image encoder for converting the spatial frame into a first image encode, and a first projector for converting the first image encode into a spatial embedding representation that can be input to the language model.SELECTED DRAWING: Figure 2

The present invention provides an information processing method, etc., that can suitably design dialogue tasks for language learners. [Solution] The information processing method involves a computer that acquires learning information representing the content that a learner of a target language should learn, and inputs a first prompt containing the learning information into a language model to generate a specification for a dialogue task to be performed by the learner. Preferably, a second prompt containing the generated specification is input into the language model to generate an instruction document that instructs the learner to perform the dialogue task, and a third prompt that instructs the language model to generate a dialogue sentence corresponding to the dialogue task.

A model training method and device relate to the technical field of artificial intelligence. The method comprises the steps that in the process of fine tuning of a language model, training samples are determined from one of a plurality of data subsets in each iteration, each data subset is a set of samples with the same kind of generality, and the samples of different data subsets are different in generality; and inputting the training sample into the language model to train the language model. In this way, the samples used in each iteration are common samples. And through small deviation between common samples, the understanding ability of the model to the samples is enhanced, and the adaptability of the model to various tasks is further improved.

The application discloses a natural language interaction-based airfoil generation and intelligent optimization design method and device, equipment and medium, and relates to the technical field of computers, which comprises the following steps: determining structured parameters based on a BERT model and input language; performing multi-scale analysis on initial airfoil parameters determined by an encoder and a decoder in a generator based on a VAE-GAN model to obtain a new generator and a new discriminator, iteratively obtaining a target VAE-GAN model, and determining whether a semantic analysis confidence meets a preset high confidence condition; if the condition is met and the instruction type is an airfoil generation and aerodynamic analysis type, generating an airfoil cluster by using the target VAE-GAN model; and if the condition is met and the instruction type is an airfoil intelligent optimization design type, optimizing a candidate airfoil to obtain an optimized airfoil. The airfoil can be generated without accurately inputting airfoil parameters, and the airfoil can be optimized.

The embodiment of the invention discloses a data processing method and device. The method comprises the steps that a data checking request statement input by a user in a natural language is obtained, the data checking request statement is input into a language conversion model to obtain a data checking request configuration code, the data checking request configuration code is operated to conduct data collection and data checking, and a corresponding data checking result is obtained, and inputting the data checking result into a checking result analysis model to obtain checking result analysis information, wherein the checking result analysis information comprises at least one group of abnormal data and a corresponding data exception type. According to the method, the language conversion model is used for converting the data check request statement input by the user in a natural language into the corresponding configuration code, the code writing process is omitted, the use threshold of the user is lowered, the check result analysis model is used for analyzing the data check result, automatic creation, classification and diagnosis of data problems are achieved, and the user experience is improved. The overall data quality can be improved depending on efficient data check.

A system determines a set of workflow languages which capture tasks to be executed in a corresponding workflow. The system defines a set of classes of expressivity, wherein a class of expressivity represents a workflow language. The system identifies, in the set of workflow languages, an input language and a target output language. The system determines whether the target output language is a match for the input language by comparing a respective class of expressivity for the input language and the respective class of expressivity for the target output language. The system returns information associated with whether the target output language is a match for the input language.

The invention discloses a tourism intelligent question and answer method and system based on a large language model, and relates to the technical field of semantic analysis. The method comprises the following steps: preprocessing a query text to obtain an embedded vector, mapping a graph entity into a third vector, screening and building a candidate library, extracting context features and clustering to obtain a query vector, splicing candidate entities into a knowledge vector, fusing to obtain a knowledge semantic vector, generating semantic features in combination with the context features, and inputting a language model to output a prediction result. According to the method, context features are extracted, query vectors are generated through clustering, query vectors representing core query intentions are generated, semantic dispersion is avoided, knowledge semantic vectors of candidate entity vectors and the query vectors are fused, the defect that plain text semantics lack structured knowledge support is overcome, semantic feature vectors are generated in combination with the context features, and the query intentions are extracted. Inputting the language model to output a prediction result; the problem that ambiguity occurs to user reply due to the fact that common sense and context background information behind words are difficult to use is solved.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice synthesis method, device, equipment and medium based on acoustic branch weight, which comprises the following steps: acquiring a voice prompt and a target text, extracting acoustic embedding of the voice prompt and text embedding of the target text; the method comprises the steps of analyzing acoustic features of voice prompts to determine acoustic branch weights, adjusting acoustic embedding according to the acoustic branch weights to obtain weighted acoustic embedding, fusing the weighted acoustic embedding and text embedding to generate fused features, inputting the fused features into a language model to generate a voice marking sequence, and decoding the voice marking sequence into a target voice waveform. According to the method, acoustic embedding and text embedding can adaptively reflect the rhythm, tone and semantic information of the voice through acoustic feature analysis and a weighted fusion mechanism, so that the naturalness and expressive power of the voice are improved while the semantic consistency is kept, and the method is suitable for high-fidelity voice generation of dialects and complex acoustic scenes.

A method for changing at least one lighting state by voice command comprises receiving and inputting the voice command into a language model, interpreting and breaking down the voice command into at least one subtask, obtaining at least one lighting feature of at least one lighting object and at least one setting option of the lighting feature, determining at least one lighting object and at least one lighting feature of the determined lighting object by inference with a trained machine learning model involving input of the lighting feature, the setting option and the subtask, creating a sequence sheet by inference with the trained machine learning model involving input of the determined lighting object, the determined lighting feature and / or the setting option and changing the lighting state in accordance with the sequence sheet. A computer system is configured to execute the method, with a processor executing a computer program product comprising sections of software code.

A respective label set is obtained for a number of translation unit pairs, with each pair comprising a set of language elements in a first language and the translation of the set of language elements to a second language. The label set includes values of one or more translation customization attributes. A value of such an attribute associated with a translation request is identified. A translated version of an input set of language elements indicated in the translation request is generated in accordance with the value of the attribute, using a machine learning model trained with the help of the label sets.