477 results about "Polyglot" patented technology

The invention discloses a method for enhancing dynamic knowledge retrieval based on a large language model, belongs to the field of knowledge retrieval, and aims to solve the problems of knowledge solidification, insufficient timeliness and illusion of a traditional LLM (Logistics Language Model). A multi-granularity knowledge base is dynamically constructed, and a rule and semantic partitioning technology is combined, so that a text is converted into a normalized vector, and a hybrid index is established; a two-channel retrieval triggering mechanism is adopted, keyword matching scores and BERT semantic probability analysis are fused, and retrieval requirements are intelligently judged; vectorization retrieval is realized through a BGE-M3 model, and candidate results are reordered in combination with a cross encoder to improve the precision. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updating and version control. According to the method, the answer timeliness and accuracy are remarkably improved, the context coherence of multiple rounds of dialogues is optimized, the method can be widely applied to the fields of intelligent customer service, professional questions and answers and the like, the LLM illusion risk is effectively reduced, and the knowledge traceability is enhanced.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

Aspects of the present disclosure relate to detecting hallucinations in language model outputs. Embodiments include receiving a user query. Embodiments further include prompting a language processing machine learning model to generate responses to the user query in each language of a set of multiple languages. Embodiments further include receiving the responses from the language processing machine learning model in response to the prompting. Embodiments further include creating embedding representations of the responses. Embodiments further include calculating, based on the embedding representations, a degree of semantic similarity between the responses. Embodiments further include determining that a response of the responses contains a model hallucination based on comparing the degree of semantic similarity between the responses to a threshold.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention relates to the technical field of data processing, in particular to a multi-language intelligent query method and device, equipment, a medium and a programmer.The method comprises the steps that a query request input by a user in a specific natural language is obtained; determining a corresponding natural language type according to the query request, extracting key information of the query request, and generating a corresponding SQL query instruction according to the key information; and querying a preset database according to the SQL query instruction to generate a query result. Therefore, the problems that in the prior art, only natural language query of a single language is supported, user requirements in a multi-language environment cannot be met, and contextual information in query is difficult to accurately capture and understand, so that data processing accuracy is low and the like are solved.

The invention discloses a dynamic interactive prompt sequence control method for chemical production, and relates to the field of flow industrial processes. Based on a state transition rule of a Phase functional block, linear execution and conditional jump of sequential control steps in a polymerization reaction temperature step control scene are realized through SFC programming, interactive processing and SFC logic steps are bound, and an interactive dialog box is triggered in a unified manner through an operation guide window. And the main program and the subprogram realize abnormal state linkage through an FAILIDX mark, and automatically detect a fault clearing state when the abnormity is recovered. The interactive processing dynamically generates an option list, supports multi-language switching, verifies input values in real time, and provides historical suggested values. The method aims at solving the problems that in a traditional sequence control system, the coupling degree of interaction logic and a core control program is high, operation interfaces are not uniform, and the misoperation risk is high, transplanting of cross-control systems and cross-process packages is supported, and the secondary development cost is remarkably reduced.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing text-to-speech modeling. In some implementations, a computer device receives input text in a first language to convert to a desired speech in a second language. The computer device receives one or more criteria for modifying the desired speech and converts the input text to a desired text in the second language. The computer device generates audio representations of the desired text in the second language and predicts, for each of the audio representations of the desired text and using the one or more received criteria, a pitch value and a duration value. The computer device generates the desired output speech using the predicted pitch value and the predicted duration value for each of the audio representations and provides the desired speech for output.

The invention discloses a multi-modal-based multi-language custom instruction identification method and system, and the method comprises the steps: S1, constructing a multi-modal large model, and training the model, the multi-modal large model constructing a support input processing unit, a query input processing unit and a measurement discriminator; s2, on the basis of the step S1, registering a user-defined voice instruction; wherein a support feature vector of a user registration instruction is acquired and stored through the support input processing unit trained in the step S1; s3, user voice is obtained, and reasoning and instruction recognition are conducted through the model generated in the step S2; wherein the trained query input processing unit is used for processing the user voice to output a query vector, and the matching degree of the query vector and a pre-stored support feature vector is calculated, so that a user voice instruction is recognized. According to the method, efficient and extensible multi-language self-defined instruction recognition is realized based on a multi-modal fusion and metric learning framework.

The invention provides a link monitoring method and device for a distributed system, electronic equipment and a medium, and the method comprises the steps: capturing the operation state of an application program corresponding to a monitoring agent program based on the monitoring agent program installed at each computing node of the distributed system according to a preset link tracking protocol, corresponding operation information is obtained; wherein the application programs installed in the computing nodes are developed based on multiple programming languages; converting the operation information of the running state of each application program into tracking data in a uniform format; sending the tracking data to a monitoring platform; wherein the monitoring platform is used for analyzing tracking data of different application programs to identify an application layer event, and the application layer event is used for being associated with network layer transmission data to realize link monitoring. According to the method, full-link monitoring can be realized in a multi-language and multi-protocol scene, and the method is suitable for a complex application scene.

The invention discloses an English text automatic proofreading system and method based on intelligent AI, and relates to the technical field of natural language processing, and the method comprises the steps: inputting an original English text, generating a syntactic graph through dependency syntax analysis, calculating a node syntactic centrality weight, and generating a triple description set after screening key nodes; on the basis of the triple description set, constructing a structured prompt template, executing English-in-English bidirectional conversion verification through a multi-language model, generating an initial image, and outputting an aligned final image through iterative optimization circulation; and searching a syntax compliance candidate word library for the conflict text node set, selecting a candidate word with the highest comprehensive score, and outputting a corrected text, a video stream and an error analysis report. According to the method, the text assertion and the visual assertion are synchronously verified through the differentiable grammar rule matrix, and the problem that context sensitive errors are difficult to detect through a traditional method is solved.

The invention relates to a multi-language large model translation performance optimization method and system based on prompt strategy activation, and belongs to the field of natural language processing. In a low-resource language environment, the translation performance of a multi-language large model still has insufficiency. The understanding ability of a large model on low-resource language input semantics is limited, so that part of potential translation ability of the large model is not fully activated, in order to relieve the influence, prompts are screened from a high-quality corpus based on four prompt strategies, a context learning template is constructed to serve as large model user input, and therefore the user input of the large model is improved. Task guidance serves as system input, and finally performance optimization of a large model in a low-resource language translation task is achieved by dynamically adjusting a prompt strategy and model parameters. According to the method, the potential translation ability of a large model can be effectively activated, and the translation quality of the large model in a low-resource language environment is remarkably improved.

The invention relates to the technical field of big language model dialogue system optimization, in particular to a big language model dynamic dialogue history compression method and system.The method comprises the steps that the maximum length of a context window matched with a target big language model, the maximum number of newly-generated lexical elements and the size of a safety buffer area are set, and then dialogue history is loaded; initial compression and verification are carried out through a keyword and TF-IDF mixed scoring system, multiple times of dynamic compression are carried out according to gradients if the conditions are not met, and finally, parameters are adjusted to adapt to the residual space when a model is called to generate response. The system comprises a dialogue history loading module, a parameter configuration module, a dynamic compression engine module and a large language model integration module. Through a multi-stage compression verification mechanism and a progressive multi-stage dynamic compression strategy, super-long texts such as engineering technology documents can be processed, service interruption is reduced, the compression efficiency is improved on the premise that key semantics are reserved, and multi-language dynamic compression is supported. The problems of system token overrun and service instability in the prior art are solved.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention relates to the technical field of smart home, and discloses an equipment control method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring an original control instruction set; performing multi-language expression conversion on each original control instruction to generate various variant control instructions matched with the original control instructions, and executing the same target operation on the variant control instructions and the original control instructions; for any variant control instruction, role conversion is carried out on the variant control instruction, and a voice control instruction set corresponding to each role is generated; and training the target control model by using the voice control instruction set, so that the target control model responds to the voice control instruction set and controls the target equipment to execute the target operation. By implementing the technical scheme of the invention, the adaptability of the smart home device to different user language habits and role identities is improved, and the naturalness and flexibility of voice interaction are enhanced.

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

Technologies for language agnostic OCR extraction include identifying a word region of an image using optical character recognition, applying a language agnostic machine learning model to the word region, where the language agnostic machine learning model is trained on training data including a set of image-text pairs and a set of multilingual text translation pairs, receiving, from the language agnostic machine learning model, a word region embedding that is associated with the word region, searching a multilingual index for a text embedding that matches the word region embedding, receiving, from the multilingual index, text associated with the text embedding; and outputting at least one of the text or the text embedding to at least one downstream process, application, system, component, or network.

The invention discloses an incremental multi-language text recognition method and system based on shared knowledge mining, and relates to the technical field of text detection and recognition. Sending the playback set and the data set of the current language into all characteristic recognizers, and mining potential shared characters and shared words among languages based on the prediction consistency of all the characteristic recognizers; based on the shared characters, the shared words and a language domain discriminator, mining a dependency relationship between incremental languages to obtain language probabilities of character levels and word levels, and based on the language probabilities, weighting prediction probability distribution of each characteristic recognizer to obtain character level and word level probability distribution after relationship enhancement; and performing argmax operation on the word level probability distribution after relation enhancement, and selecting an index corresponding to a maximum probability value as a final recognition result. The forgetting of old language knowledge in the incremental learning process can be reduced, so that the continuous learning ability of the text recognizer is improved.

The invention relates to a method for improving the internal accuracy of a response from an artificial intelligence (AI) trained in multiple languages, in particular a generative AI trained in multiple languages. The invention further relates to a data processing system for carrying out the method.

A method for generating a first case dataset in a first language. The method includes receiving adverse event data. The method further includes determining case data including general case data and regional case data and providing the case data to a translator computing device to enable display on a user interface including multiple duolingual text fields with a first language text field including at least a portion of the text data in the first language and a second language text field adjacent the first language text field. The method further includes receiving the text data in the second language from a translator computing device. The text data in the second language is received via the second language text fields of the plurality of duolingual text fields. The method further includes generating and outputting the first case dataset including the text data in the first language.

The invention provides an information processing method and device, a model training method and device, equipment, a medium and a product, and relates to the technical field of computers. The public opinion information processing method comprises the steps that on the basis of a first language model, a crawled original language text is coded to obtain an original language vector, on the basis of a second language model, a target language text is coded to obtain a target language vector, the target language text is generated by translating the original language text, and the first language model is a multi-language model; generating a sentence-level first fusion vector and a word-level second fusion vector based on the original language vector and the target language vector; obtaining corresponding theme features and emotion features based on the first fusion vector; performing entity extraction on the second fusion vector to obtain entity features; and generating a public opinion analysis result based on the subject features, the emotion features and the entity features. According to the technical scheme, the public opinion association capture capability is enhanced through multi-feature joint analysis, and the public opinion analysis efficiency and accuracy can be improved.

The invention discloses a multi-language data analysis-oriented extensible system, method, equipment and medium, and relates to the technical field of multi-language analysis, the multi-language data analysis-oriented extensible system comprises a grammar analysis module used for analyzing a query language and generating a language-specific syntax tree structure after analysis; the grammar adaptation module is used for converting a language-specific grammar tree structure into an abstract grammar tree AST in a uniform format; the abstract syntax tree processing module injects a permission control strategy, a field desensitization rule and an alarm exception expression into the AST; the business processing module is used for executing business-related rule check and label injection; the query plan generation module is used for constructing a logic query path and a corresponding distributed execution scheme; and the task scheduling module issues the distributed execution scheme to the corresponding execution node. According to the method, by constructing a unified multi-language grammar analysis framework and a standardized abstract syntax tree AST representation mechanism, compatibility and fusion of multiple query languages such as SQL, SPL and natural language are achieved.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention discloses a multi-language text self-adaptive configuration method and electronic equipment, and relates to the technical field of computers, and the method comprises the following steps: obtaining a source text set in response to a translation instruction received when a page runs, and translating the source text set according to a target language identifier and a constraint strategy to obtain a translated text set; according to a constraint strategy, carrying out adaptability detection on each translation in the translation set to obtain an adaptive set and a non-adaptive set; performing semantic rewriting on each non-adaptive translation in the non-adaptive set to obtain a target candidate set; and aggregating the adaptation set and the target candidate set in the same rendering frame, and rendering and displaying the adaptation set and the target candidate set in batches, so that the problems of lack of multi-language dynamic adaptation capability and insufficient semantic equivalent compression are solved, the accumulated layout offset is remarkably reduced on the premise of ensuring semantic integrity and readability, and the layout efficiency is improved. And the page stability and the user experience are improved.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention relates to the technical field of artificial intelligence, provides a multi-language task execution method, device and equipment and a medium, is applied to financial and medical health care service scenes, and can acquire text data and visual action data of multiple languages according to an execution instruction and perform preprocessing to realize standardized processing of multi-modal data; constructing a cross-language word vector semantic space based on adversarial training according to the multi-language features to realize preliminary word vector alignment; performing multi-language semantic alignment on the multi-language features according to a cross-language word vector semantic space, further breaking language barriers, and realizing depth mapping and alignment among different language semantics; a multi-language culture knowledge graph is constructed, so that culture knowledge is introduced, and the culture perception ability is improved; fusion is carried out through a gating fusion mechanism, fusion features with language attributes and cultural attributes can be obtained, and therefore multi-language tasks can be executed more accurately by integrating multi-language information and cultural knowledge.

A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.

The invention provides a code analysis method, system and equipment based on a multi-programming language sandbox and a medium, and belongs to the technical field of code analysis and detection.The method specifically comprises the steps that an input code and a corresponding unit test sample are obtained, and a programming language type specified by the code is determined; sending the code and the unit test sample to a sub sandbox environment of a corresponding language for execution; a compiler in the sub sandbox reports a missing library according to a code compiling result and prompts a user to install the missing library; calling an analysis tool of a code analysis module to analyze the code; compiler feedback of the sub sandboxes and various analysis results generated by the code analysis module are integrated into a large language model; inputting the integration result into a large language model through a preset template; and analyzing and evaluating the code case based on the user instruction. Through multi-language sandbox isolation, automatic analysis tool integration and large language model enhancement, the security, efficiency and quality of a code processing flow are improved.

The invention discloses a method and a system for asynchronous execution of JavaScript at a server side, and relates to the technical field of computer software, the method comprises the following steps: constructing a multi-language runtime context pool on the server side based on GraalVM, and pre-compiling a script and a core class; when the script is loaded, asynchronous execution strategy parameters are injected into the corresponding isolation context; calculating the fragmentation number of the overall task submitted by the script, and carrying out overall task fragmentation; mapping the fragments to the corresponding isolation assembly lines according to the fragment execution categories, constructing feature vectors according to the fragments in the isolation assembly lines, calculating fragment priorities, and executing in sequence; after fragment execution is completed, execution time delay is collected, historical time delay prediction is updated, and when conditions are met, isolation retry is carried out, and compensation fragments are generated. Through pre-compilation, asynchronous execution strategy, adaptive fragmentation and priority scheduling, and compensation fragmentation generation, fault self-recovery and service continuity guarantee with low time delay response, high scheduling flexibility and controllability and automation are realized.

The invention provides a cross-language information data collection and structured processing method based on adaptive learning, and relates to the technical field of artificial intelligence natural language processing, and the method comprises the steps: collecting information data through a cross-language information data source, and carrying out the preprocessing; the preprocessed cross-language text data is input into a self-adaptive learnable semantic model of a neural network framework combining dynamic feature extraction and meta-learning strategies for feature extraction and classification, the semantic model is trained and optimized based on a covariance matrix prediction network, the disturbance direction is dynamically adjusted, and the feature extraction and classification are performed. Enabling the semantic model to adaptively adjust a feature extraction mode according to different contexts in a multi-language environment; and extracting structured information of a cross-language text through a natural language processing technology, and performing cross-language entity alignment and normalization on the feature representation output by the semantic model to generate a unified data structure representation. According to the invention, the cross-language data processing efficiency and accuracy are improved.

A method of enabling a language model trained in a first language to support a second language includes extending an existing vocabulary of the language model to include additional tokens for text in the second language. The method also includes initializing the additional tokens for text in the second language based on subtokens of tokens for text in the first language from the existing vocabulary. The method further includes training the language model using a mixed language dataset that includes a first language corpus and a second language corpus. In addition, the method includes performing instruction tuning using a dataset that includes (i) instruction and response pairs involving the first language and (ii) instruction and response pairs involving the second language.