438 results about "Multiple language" patented technology

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

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 invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

Systems and methods for generating speaker video and audio in multiple languages for videoconferencing are provided. For example, a computing device can access a speaker speech audio signal that includes a speaker speech in a first language, a video of the speaker and a translated speech audio signal of the speaker speech in a second language. The computing device generates, based on the translated speech audio signal, a converted translated speech audio signal that includes a speech in the second language having voice characteristics in the speaker speech. The computing device further generates a lip-synched speaker video based on the video of the speaker and the converted translated speech audio signal. Lip movements in the lip-synched speaker video correspond to the converted translated speech audio signal. The converted translated speech audio signal and the lip-synched speaker video are transmitted to a video conference provider configured to host the video conference.

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 document information extraction service can utilize an LLM to provide support for extracting information from documents in multiple languages. A trained first machine learning model can map data extracted from a first master document of a first document type in a first language. The mappings can be corrected via user input to obtain ground truth data for the master document. The ground truth data can be translated into a second language and optionally corrected to obtain translated ground truth data. An LLM can generate a training dataset of fake documents of the first document type that contain text in the second language based at least in part on the translated ground truth data. The trained first machine learning model can be trained further with the training dataset and deployed to extract data from documents of the first document type that contain text in the second language.

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 and method for classification. A method includes identifying candidate entities among text data by applying at least one entity identification rule to the text data. Inputs are constructed based on the identified candidate entities, where each input includes a first portion of text indicating a candidate entity and at least one second portion of text and where the at least one second portion of text of each input is adjacent to the first portion of text of the input. Multiple language models are applied to the inputs, where each language model is trained to identify a respective set of entities and where outputs of the language models include at least one portion of entity-indicating text for each input. Based on the outputs of the language models, at least one named entity in the text data is determined.

Various embodiments disclosed relate to a method of producing an analysis document using generative artificial intelligence, the method comprising: maintaining a database containing multiple language learning models and a collection of prompts; generating a patent prosecution tool user interface integrated with a generative artificial intelligence program; receiving a user input comprising a matter associated with at least one matter dataset, where the matter dataset includes a plurality of patent documents associated with the matter; selecting a language learning model from the database; retrieving a plurality of patent documents for display on the patent prosecution tool user interface; loading at least one of the plurality of patent documents into the artificial intelligence program; displaying more than one prompt from the collection of prompts; initiating a script in the generative artificial intelligence program based on a user selected prompt and the at least one patent document; analyzing the patent document according to the script to produce a preliminary analysis; producing the analysis document according to the preliminary analysis; saving the analysis document.

The invention particularly relates to a code writing, checking and modifying method based on a large model. According to the code writing, checking and modifying method based on the large model, a fine-tuning large language model is adopted to analyze user requirements, and multi-language initial codes are generated; executing deep code review through a static analysis engine, wherein the deep code review comprises syntax tree verification, security vulnerability mode matching and API dependency chain detection; executing the automatically generated test case set in an isolation environment, monitoring operation indexes in real time, and generating a defect thermodynamic diagram; and driving the large model to perform iterative code reconstruction based on a defect analysis result until it is confirmed that a preset quality standard is met. The code writing, checking and modifying method based on the large model is suitable for multiple languages, the defect rate of codes is reduced, the software development period is prolonged, the labor cost is reduced, and the vulnerability detection rate is greatly increased.

The present disclosure provides a method of extracting information from an image of a document in which the document image is properly aligned to be processed, the regions containing the desired information are detected and extracted from the document image, a text machine-learning model is performed in which the handwritten text in multiple languages may be extracted and stored, and a user may review, edit, and translate the extracted information to create a standardized digital format of the information contained in the document.

The present invention facilitates communication between sign language users and machines by translating sign language and text using AI models, deep learning computer vision, and word embeddings. Users interact via sign language, captured and processed through deep learning and NLP modules. The system converts sign language videos into text, constructs coherent sentences, and generates contextually appropriate responses using a Retrieve and Generate (RAG) model. Responses are translated back into sign language videos, spelling out words not found in the dictionary. If requested, a human agent can respond. Key features include high-accuracy recognition, context-aware response generation, dynamic vocabulary updates, and optional human interaction. The method ensures efficient processing with LLM, embedding techniques, and deep learning, optimizing translation accuracy and user experience. The system adapts to multiple languages and dialects by training on specific sign languages, making it applicable globally.

The invention discloses an uncertainty guided few-sample harmful speech detection method (U-GIFT). According to the method, a pre-training language model is finely adjusted based on a small number of labeled samples, and a semi-supervised self-training and uncertainty guiding strategy is combined. Monte Carlo Dropout is started in the reasoning stage, multiple times of random forward propagation are carried out to obtain sample posterior distribution, prediction entropy and information gain are calculated, pseudo-label samples are sorted and screened, and only high-confidence samples are selected to be added into a training set. And in order to reduce the influence of a pseudo labeling error, designing a stability weighting mechanism, giving a sample weight according to a prediction variance, and constructing a joint loss function, so that the model preferentially learns a stable sample to improve the detection performance. According to the method, the semantic and attention mechanism of the pre-training model is utilized, the detection effect is remarkably improved under the conditions of few samples, imbalance, multiple languages and cross domains, models such as BERT, RoBERTa, XLM-R, LLaMA2 and DeepSeek-R1 are compatible, and the method is suitable for content auditing and risk prevention and control.

The invention relates to a memory retrieval method, a memory generation method and related devices. The method comprises the following steps: acquiring an input search word, and translating the input search word into a search word of a target language; performing memory retrieval by using the input retrieval word and the retrieval word of the target language according to the memory index to obtain a retrieval result; wherein the memory index is established according to at least one of an original and a copy of the memory information; the copy is obtained by translating the original into a target language. The method can match and support multiple languages.

The invention discloses a text dialogue method and device suitable for multiple languages, terminal equipment and a storage medium, after a source text input by a user in a current dialogue is obtained, a target language corresponding to the source text can be accurately recognized based on a phrase structure and a sentence structure of the source text, and therefore the user can select the target language based on the recognized target language. And a corresponding grammar rule can be selected to generate a subsequent reply text. In addition, the user intention and context information can be deeply understood based on the extracted local features, global features and dialogue features, so that the reply text conforming to the language expected by the user is generated, and the generated reply text is correct in grammar based on the language of the current input text and the grammar rule of the language. And the expression is natural and smooth, the stiff or incoherent condition is avoided, the accuracy and naturalness of the reply text in the dialogue process are improved, the multilingual text dialogue is realized, and the dialogue experience of the user is optimized.

The invention is suitable for the technical field of communication, provides an interphone communication method based on artificial intelligence and a related device, and realizes simultaneous translation among different language types in interphone communication. The interphone communication method based on artificial intelligence mainly comprises the following steps: acquiring first audio data of a speech of a target user; identifying a language type corresponding to the first audio data, and obtaining a first target language type of the first audio data; if the first target language type is not consistent with a preset output language type, inputting the first audio data into a first preset artificial intelligence model for processing to obtain first target audio data conforming to the output language type, the first preset artificial intelligence model is a trained model capable of translating the input audio data of multiple languages into the output audio data of multiple preset languages; and transmitting the first target audio data to a second interphone, so that the second interphone obtains and plays the first target audio data.

Various implementations include initiating, at a client device, a translation application for translation of a dialog session between a first user speaking in a first language and a second user speaking in a second language. In many implementations, the first language, spoken by the first user, can be determined based on one or more features of the client device. Additional or alternative implementations include determining a subset of candidate second languages from a plurality of languages available to the translation application. In a variety of implementations, the system can render output based on the subset of candidate second languages, and can process received input from the second user indicative of one or more of the candidate second languages in the subset of candidate second languages.

The invention relates to the technical field of software testing, and discloses an LLM and SMT solver-based test case generation method, which is characterized in that a search type test is taken as a core drive, when coverage stagnation occurs, an execution path is subjected to semantic analysis by means of an LLM, a logic expression for describing a target path condition is generated, and in consideration of the limitation of the LLM in logical reasoning, a test case is generated; the system further introduces an SMT solver to perform verification and reasoning on a logic expression output by the LLM, finally feasible test input is generated, and the test efficiency and the coverage rate are improved. According to the method, the efficiency and precision of path coverage are improved, good adaptability and universality are achieved, the method is suitable for various languages and complex program structures, and the intelligent level and engineering practical value of automatic testing are effectively improved. The problem of insufficient accuracy of a large language model in complex path constraint reasoning is effectively relieved.

The invention provides a relay protection device interface language switching method and system based on a hot loading mechanism, and the method comprises the steps: constructing a variable-length language storage structural body under a C language according to the variable-length text characteristics of multiple languages in a language text file, so as to generate an instantiated object, and further binding an interface element of a relay protection device; under the condition that an operation signal of switching languages of the user interface is detected, obtaining a user target language according to the interface element index so as to read a corresponding language text file; pointing a first-level pointer corresponding to the instantiated object to the read language text file, and pointing a second-level pointer corresponding to the instantiated object to the first-level pointer so as to bind the interface element and the target language text file; analyzing a second-level pointer corresponding to the instantiated object, and mapping the second-level pointer to a first-level pointer corresponding to the instantiated object to obtain target language text information pointed by the first-level pointer. According to the method, interface language switching can be completed without restarting equipment.

The invention provides an eBPF program automatic generation method, system and device based on retrieval enhancement and thinking chain reasoning, and the method comprises the steps: constructing an eBPF semantic knowledge base, and extracting and structuring a plurality of program examples with semantic annotations; after a natural language task description input by a user is received, the natural language task description is coded into a semantic vector, and accurate retrieval of related contexts is achieved in combination with semantic indexes; based on a retrieval result, constructing a structured prompt, and guiding a language model to gradually generate an eBPF program according to stages; an eBPF verifier is used for carrying out legality check on the generated program, and behavior testing is carried out in combination with task input; when structural errors or semantic deviations are found, the model is guided to be automatically repaired based on error information. The method can be operated on a multi-language model platform, supports the output of two styles of BCC and BPFtrace, and has the advantages of controllable structure, accurate semantics, stable deployment and the like. Experimental results show that the automatic generation efficiency and accuracy of the eBPF program can be remarkably improved.

The invention discloses a data management method, device and system, a storage medium and a computer program product, and belongs to the technical field of computers. In the method, a first word segmentation granularity is determined based on the length of a first text, word segmentation is performed on the first text according to the first word segmentation granularity, and an index of a first file is created based on a word segmentation result. Wherein the first text comprises any one or more of a file name of the first file and a file path of the first file. Therefore, the word segmentation algorithm supports texts of multiple languages, multiple scenes and any length, and is more universal and stronger in robustness. Wherein multiple languages indicate that the text to be subjected to word segmentation is not limited to comprise any one or multiple languages, multiple scenes indicate that index establishment based on file names and index establishment based on file paths are supported, and any length indicates that the word segmentation granularity is determined based on the text length and can be adaptive to the text length. According to the scheme, the data search speed can be increased on the premise of ensuring the search accuracy, and the performance of a data search system is effectively improved.

The invention provides a low-resource multi-language large model training method and system for personalized course learning, and belongs to the technical field of large language models, and the method comprises the steps: S1, collecting training samples of multiple languages; performing de-duplication and de-noising processing on each sample, then unifying data formats, and adding language attributes as language labels; s2, initializing parameters of an adaptive sampling scheduler and a dynamic loss scheduler; and S3, taking the pre-trained large language model as a base model, and adding an adaptive sampling scheduler and a dynamic loss scheduler in the training process. According to the method, the dependence on low-resource language annotation data is reduced, and the training weights of different language samples can be adaptively balanced; and dynamically matching the multi-language task difficulty with the model learning progress.

The present disclosure generally relates to evaluating and enhancing LLM responses. In some implementations, a system includes multiple language models with different specialized roles that work together to improve response reliability and transparency. A responder model can generate initial responses to user queries, providing diverse perspectives on the same input. An evaluator model can assess and combines responses from the responder models into an accurate and reliable output. A reporter model can generate summaries and alerts about response quality and confidence levels, providing transparency to users about the decision-making process. An artificial intelligence (AI) engine can manage the flow of information between the different models, orchestrating their interactions and ensuring proper sequencing of operations. A retrieval system can provide additional context from external knowledge sources, allowing the system to generate accurate and well-informed responses.

The invention relates to a site text translation method and device, equipment and a medium. The method comprises the steps that a translation data obtaining request is responded, corresponding translation data are called according to a target language set in a site theme of a currently accessed independent site, and the translation data comprise translation content corresponding to at least part of translation keys in a current site page; determining uniquely corresponding translation content from the translation data according to a reference path represented by the translation key; identifying whether the translated text content contains a variable placeholder or not, and correspondingly replacing the variable placeholder with a predetermined variable actual value of the site page to generate a final translated text; and injecting the final translation into the corresponding position of the corresponding translation key so as to display the final translation in the current site page. According to the method, the efficient, flexible and accurate site text translation effect is achieved, and the user experience and development and maintenance efficiency supported by multiple languages of the independent site are remarkably improved.

The invention discloses an image-text report generation method fusing a multi-mode large language model and an RAG mechanism, and belongs to the technical field of text processing. The method comprises the following steps: firstly, converting a PDF document into an image, identifying and extracting contents such as texts, tables and charts through a multi-modal model, and constructing a searchable knowledge fragment library; then, based on user query, adopting a hybrid retrieval strategy to obtain related evidence, and utilizing a large language model to generate a Markdown report containing an image placeholder; and meanwhile, a text graph module is called to generate an illustrated graph, and finally visual report output of image-text fusion is realized. The method supports multi-modal content understanding, cross-modal retrieval and collaborative generation, has good generalization, accuracy and practicability, and is suitable for multi-field and multi-language complex document processing and report generation.

The invention provides a language recognition method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: carrying out the sliding extraction of a voice segment from a to-be-recognized voice according to a sliding window of a preset size, and detecting a voiceprint turning point from the to-be-recognized voice; under the condition that the voiceprint turning point is detected from the voice segment in the first sliding window, carrying out displacement adjustment on the first sliding window according to the voiceprint turning point so as to enable the voiceprint turning point to be located at the end point position of the first sliding window; and performing language recognition on the shifted voice segment in the first sliding window to obtain a language recognition result. According to the scheme, the voiceprint turning point in the sliding window can be detected, the jumping moment of the speaker in the sliding window can be recognized, the sliding window is moved according to the jumping moment to divide the voice segments, language recognition of the voice segments of multiple languages and a single language in the sliding window can be avoided, and the user experience is improved. Therefore, the accuracy of language recognition of the to-be-recognized speech is higher, and the accuracy of speech translation is further improved.

The embodiment of the invention provides a multi-language switching method and device.The method comprises the steps that under the condition that a multi-language item is compiled, all translated texts in the multi-language item are translated to obtain a multi-language resource file; under the condition of running the multi-language project, receiving a switching instruction carrying a target language, and obtaining a target language resource file corresponding to the target language from the multi-language resource file; and querying from the target language resource file to obtain a target translation text corresponding to each translated text, and replacing the translated text with the target translation text. According to the method, all the translated texts are automatically translated in the project compiling stage, the multi-language resource files can be generated after compiling is completed, language resource switching can be automatically completed during project running, existing service codes do not need to be manually modified, and therefore the labor cost of multi-language / internationalized project development is saved.

The invention provides a multi-agent collaborative cross-language system translation method and system based on loAs, and relates to the technical field of intelligent translation.The method comprises the steps that to-be-translated text information and conference theme information are obtained, and first fusion semantic information is obtained; obtaining first target semantic information through the first translation agent; obtaining second target semantic information through a second translation agent; obtaining second fusion semantic information through the first target semantic information and the conference theme information, and determining a second target language translation text; and acquiring third fused semantic information through the second target semantic information and the conference theme information, and determining the first target language translation text. According to the method and the device, the translation accuracy of key texts can be improved by referring to conference theme information, and multi-language semantic information can be mutually corrected through a cross attention mechanism to improve the semantic consistency of multi-language translation, so that the overall translation accuracy is improved, and the communication cost is reduced.

The invention relates to a Web multi-language translation method and device based on AST analysis. After operation information of the user for the function module of the page is received, the language type corresponding to the user is determined, the internationalized calling function in the source code of the function module is called according to the language type, a translation is obtained, and the page of the function module is generated according to the translation and presented to the user. The method comprises the following steps of: analyzing an abstract syntax tree of a source code of a functional module in advance, determining an original text of a copywriting and position information of the original text in the source code, translating the original text in various language types to obtain translated texts of various language types, and generating an internationalized calling function comprising the translated texts of various language types; and replacing the to-be-translated copywriting with the internationalized calling function in the source code according to the position information. According to the method provided by the embodiment of the invention, large-scale manual search and replacement are avoided, and the maintainability and expansibility are very high.

The invention provides a voice generation method and device, a computer readable storage medium and electronic equipment, and the method comprises the steps: obtaining an input text which represents a dialect text of a target small language; converting the input text into a standard text through a finite state conversion model; the standard text is analyzed through the target voice model, audio features of the standard text are obtained, target voice is generated according to the audio features, each group of data in the multiple groups of data comprises historical standard texts and historical audio features, the historical standard texts comprise general texts and special texts, and the historical audio features comprise general audio features. The general text represents the standard language text contained in various languages including the target small language, and the special text represents the standard language text only contained in the target small language. According to the method and the device, the problem that the voice corresponding to the complex small language cannot be accurately generated is solved, and the effect of accurately generating the voice corresponding to the small language is achieved.