26 results about "General-purpose language" patented technology

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

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

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

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

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

The invention belongs to the technical field of artificial intelligence, and particularly relates to a fine tuning model construction and multi-round incremental training framework oriented to power grid innovation management, which is characterized in that power grid multi-source heterogeneous data is introduced to carry out pre-training on the basis of a general pre-training model, and the core is to design a text-time sequence joint masking loss function to obtain a fine tuning model; while the understanding capability of the universal language / time sequence structure of the model is reserved, the model deeply masters the professional characteristics of the power grid, and a basic model with both the universal adaptability and the professional property of the power grid field is constructed. According to the invention, through modularization, low-rank increment, dynamic sparse and rolling increment training and strict online risk management and control, an efficient, controllable and sustainable evolution model training framework oriented to a multi-service scene of the smart power grid is constructed, a landing engineering solution is provided for stable, efficient and sustainable operation of the smart power grid, and the development of the smart power grid is facilitated. The method plays an important role in promoting digitization and intellectualization of a power grid.

This invention discloses a heterogeneous hybrid expert large model architecture and alignment method for high-concurrency online medical dialogue. First, the method proposes a dual-pool heterogeneous routing strategy of "4 general experts + 2 departmental experts." Through physically isolated parallel routing channels, it forces the model to simultaneously activate general language capabilities and specialized medical reasoning when processing input, fundamentally ensuring the focus and professionalism of responses. Second, it adopts a structural evolution strategy based on parameter expansion and selective freezing. By completely freezing the pre-trained basic parameters and training only newly added departmental experts, it completely eliminates catastrophic forgetting. Finally, in the model alignment stage, it employs the Group Relative Policy Optimization (GRPO) algorithm for valueless models to reduce memory overhead and designs a dual-track hybrid reward function combining low-level semantic similarity rewards and high-level structured large model referee scoring, effectively solving the "reward hacking" problem and guiding the model to generate refined responses that combine clinical accuracy and humanized interaction. This invention significantly improves the performance of medical dialogue systems. Its sparse activation and parameter freezing characteristics further ensure low latency and high throughput in online high-concurrency scenarios, providing a complete solution for the deployment of reliable medical dialogue systems.

This invention discloses a method, apparatus, terminal device, and storage medium for TCM knowledge question answering based on retrieval enhancement generation, belonging to the field of artificial intelligence. The method comprises: encoding a TCM-related query input by a user to obtain a first encoding vector; invoking a retrieval engine in a preset retrieval enhancement generation platform, causing the retrieval engine to calculate similarity based on a built-in TCM knowledge vector database and the first encoding vector, sorting all similarity scores from highest to lowest, and selecting several top-ranked TCM prescription files as target TCM prescription files; the TCM knowledge vector database stores several TCM prescription files; and inputting the target TCM prescription files and the query statement into a preset large language model to obtain TCM knowledge question answering results. By implementing this application, the problem that existing general-purpose language models cannot meet users' needs for professional TCM consultation can be solved.

This application provides a method, apparatus, storage medium, and electronic device for automated identification of customs declaration documents, relating to the field of automated information processing technology. The method includes: identifying the document type of the documents to be identified based on a general language model, determining the document type of each document among multiple documents to be identified; converting each document to be identified into a document image, and performing image preprocessing operations on each document image to obtain a target document image for each document to be identified; and using a visual language model corresponding to the document type to identify and extract key information from the target document image of each document to obtain the target information corresponding to each document to be identified. The automated identification method for customs declaration documents provided by this application can quickly extract the required information from the original customs declaration documents and convert it into structured data for subsequent processing and analysis, greatly reducing labor costs and improving customs declaration efficiency.

The invention provides a method and system for enhancing online speech recognition based on online OCR, and relates to the technical field of speech recognition, and the method comprises the steps: capturing a shared digital image in a collaborative session online in real time, and extracting a text in the shared digital image; performing word segmentation processing on the extracted text to obtain a first vocabulary set; training and generating a session exclusive language model corresponding to the collaborative session based on the first vocabulary set; fusing the session exclusive language model and a pre-created universal language model to obtain a corresponding fused language model; performing conversion processing on the fused language model to obtain a corresponding language model decoding graph; in the decoding stage, a language model decoding graph and a pre-created acoustic vocabulary decoding graph are loaded, the two loaded decoding graphs are dynamically decoded in real time, a text recognition result of the voice in the collaborative session is obtained, and the acoustic vocabulary decoding graph is created in advance based on a universal acoustic model and a universal language model. The objective of the invention is to improve speech recognition precision and efficiency.

The application relates to a text management method and device, computer equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: obtaining a target text, performing data preprocessing on the target text to obtain a semantic vector corresponding to the target text; classifying the target text according to the semantic vector and a trained general language model to obtain a target classification result of the text type of the target text; determining a target storage path corresponding to the target classification result according to a mapping relationship between a storage path and a classification result; and storing the target text according to the target storage path. The method can improve the document management efficiency.

A system is provided.SOLUTION: A system comprising: means for receiving information entered in different languages; means for translating the received information into a common language; means for checking and modifying grammar of the translated information; means for consolidating a plurality of information; means for summarizing the consolidated information; and means for reporting the summarized information to an administrator.SELECTED DRAWING: Figure 1

This section describes a system and techniques for processing spoken language for use as input to a control system. After an utterance is received from a user in a general language, a model of a generative neural network is invoked to transform the utterance into an expression corresponding to a domain-specific language. The expression in the domain-specific language is then fed to a control system that accepts expressions of the domain-specific language as input and controls a device based on that input.

The invention discloses a semiconductor-oriented semantic map construction and natural language query method and system, and belongs to the field of natural language processing. The method comprises the following steps: constructing a semantic map offline, extracting metadata of a semiconductor production line database, mapping the metadata into a map skeleton, and creating Data node enhancement in combination with field data; analyzing a natural language problem on line, extracting entity links to atlas anchor points, and expanding and pruning to obtain context sub-graphs; after the sub-graphs are serialized, the sub-graphs, NLQ and task instructions form dynamic cue words, and the dynamic cue words are input into a large language model to generate SQL; the system comprises an atlas construction module, a subgraph retrieval module, a cue word generation module, an SQL generation module and a data storage module. According to the method, the relationship between entities can be clearly represented, domain ontology knowledge is fused, professional terms and general languages are distinguished, LLM reasoning difficulty is reduced, SQL generation precision is improved, and the method is particularly suitable for knowledge-intensive scenes such as semiconductor manufacturing.

A system, article, and method of automatic context-bound domain-specific speech recognition uses general language models.

An online system automates the generation and presentation of video content for physical objects. The system stores object data and 3D models for users, including models of objects for sale. Users can generate and manage visual markers on 3D models to highlight specific features. The system generates videos depicting the physical objects using the stored models and object data, and produces synchronized audio voiceovers describing the objects' features. Script generation prompts are created based on object data and input to a generative language model to generate scripts for the voiceover. The system generates voiceover audio data using text-to-speech or generative AI models and combines the audio with the video. The resulting video and audio data are transmitted to a client device for user presentation and interaction. The system provides an integrated workflow for creating, editing, and presenting detailed, object-specific video content with automated narration.

The invention discloses a special model fine tuning and training method for a generative marketing scheme in the field of content marketing. The method comprises the following steps: collecting and preprocessing multi-source marketing data; performing feature extraction and semantic annotation to generate a semantic annotation data set; calling a pre-trained universal language generation model, and outputting an adaptive generation model; performing supervised fine tuning training to obtain a fine tuning model; weight parameters of the fine tuning model are optimized through an improved RL-CEM algorithm, and an enhanced training generation model is obtained; outputting a marketing generation content set; establishing a model evaluation and feedback mechanism, and generating a final special generation model; according to the method, marketing release feedback data is collected in real time to trigger a retraining mode, a self-adaptive model training mechanism is formed, self-adaptive model optimization is achieved, the semantic consistency, the marketing matching degree and the conversion effect of generated content can be effectively improved, and the content marketing automation and intelligence level is remarkably improved.

A computer-implemented method is disclosed, comprising: receiving, by one or more large language models, LLMs, one or more natural language system prompts which, when processed, causes the one or more LLMs to perform a system method. The system method comprises: detecting the source language of a source text, wherein the source text is provided in natural language; determining, based on a user prompt, a target language; generating, a first translation by translating the source text from the source language to the target language; generating, a back translation by translating the first translation from the target language to the source language; comparing, the back translation with the source text to determine one or more inconsistencies; determining whether the one or more inconsistencies exceed an error threshold; based on the one or more inconsistencies exceeding the error threshold, generating, based on the one or more inconsistencies, a second translation by translating the source text from the source language to the target language; and based on the one or more inconsistencies not exceeding the error threshold, outputting, to a user, the first translation; processing, by the one or more LLMs, the one or more system prompts; receiving, by the one or more LLMs, via a user device, a user prompt comprising the source text; and providing, by the one or more LLMs, using the system method, the first translation or the second translation.

Embodiments of the present application disclose a SOC path verification system and method, an electronic device and a storage medium, comprising: a first subsystem realized based on a hardware description language, and a second subsystem realized based on a general programming language; wherein the first subsystem is configured to acquire a SOC path test case realized by the general programming language, and each test stage in the SOC path test case, and start a plurality of test processes corresponding to each test stage respectively; the second subsystem is configured to determine a target general language function corresponding to each test process respectively; each target general language function corresponding to the same test stage is called concurrently by each test process corresponding to the same test stage, a stage test result corresponding to each test stage is obtained, and a path verification result is generated according to each stage test result, which not only guarantees the stability and reliability of the SOC multi-path concurrent verification process, but also improves the efficiency of the SOC multi-path concurrent test.

The present application relates to the field of information technology, and in particular to an automatic protein property prediction method and system based on an agent. The method and system comprise: receiving a user input protein analysis task instruction, decomposing the analysis task instruction; task analysis is carried out on the decomposed analysis task instruction, a custom function for protein understanding task is accessed, the core requirement of the protein analysis task instruction is identified, and the required analysis task type is determined; according to the analysis task type, the custom function is accessed, the analysis and prediction of protein data are completed based on a general language model, and reasoning results are generated. The present application can automatically execute protein analysis tasks, realize automatic processing from user instructions to analysis results, thereby significantly improving protein analysis efficiency and reducing user usage threshold.

This invention belongs to the fields of deep learning and robot control technology, and provides a visual language navigation method based on enhanced endpoint alignment to improve VLN-BERT for robot visual language navigation tasks. First, path-instruction pairs are extracted from the VLN dataset, and panoramic image sequences and natural language instructions in the path are embedded to obtain a preprocessed dataset. Based on the three-stage pre-training of VLN-BERT, the model is pre-trained through an enhanced endpoint alignment task to enhance the model's visual language alignment of path endpoints. The pre-training sequence is: general language foundation, visual foundation, action foundation, and enhanced endpoint alignment. The model, after four stages of pre-training, is fine-tuned through a path selection task to enable path selection capabilities. This invention enhances the model's visual language alignment of path endpoints by incorporating the enhanced endpoint alignment task into the three-stage pre-training process of VLN-BERT, thereby improving the navigation success rate of the agent in real-world environments.

Speech recognition may be improved by generating and using a topic specific language model. A topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. A speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. Using the retrieved corpus of text, the speech recognition device may create a topic specific language model. In one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.

The application provides a speech recognition method and device, electronic equipment and storage medium, and relates to the technical field of speech recognition. The method comprises the following steps: inputting collected to-be-recognized speech into an acoustic model to obtain a first text output by the acoustic model; when it is detected that the first text comprises an entity word marker, inputting an entity word part in the first text into an entity language model to obtain a first confidence of the entity word part output by the entity language model, and inputting the first text into a general language model to obtain a second confidence of the first text output by the general language model; the entity word part comprises the entity word marker and a target entity word; and the target entity word is stimulated based on the first confidence and the second confidence to obtain a speech recognition result of the to-be-recognized speech. The technical scheme provided by the application can avoid the false triggering of the entity word in the speech recognition process.

The application provides a cross-language question answering system construction method based on a generative multilingual model, and the method comprises the following steps: acquiring multilingual text data, pre-training a general language model based on the multilingual text data to obtain a multilingual pre-training model; acquiring a single-corpus question and answer data set, processing the question and answer data set by using a placeholder to construct a training sample for prompt learning; iteratively fine-tuning the multilingual pre-training model based on prompt learning according to the training sample to obtain a generative multilingual model; acquiring to-be-recognized text and question text, inputting the to-be-recognized text and the question text into the generative multilingual model, and generating answers in different languages corresponding to the question text based on beam search. The application constructs a question and answer system capable of realizing cross-language question answering based on a multilingual model with a transfer learning capability and easily obtained single-language corpus, and can obtain answers in a generative manner, thereby increasing the diversity of answers.

The invention relates to the field of molecular characterization learning and toxicity detection, and discloses a molecular toxicity detection method combined with a chemical language model.The molecular toxicity detection method comprises the steps that firstly, SMILES sequences of molecules and corresponding chemical text descriptions are obtained to form a chemical corpus, and the SMILES sequences are converted into molecular diagrams; selecting a general T5 language model for initialization, and training the T5 language model to obtain a chemical language model; calculating the similarity with a sample in a database to obtain context prompt information; performing molecular labeling and corresponding chemical text description; carrying out embedded learning on the chemical text description and the molecular graph, and calculating and comparing alignment loss; calculating KL divergence shared knowledge and semantics; carrying out joint training to obtain an overall model, and optimizing parameters; detecting the toxicity of the biomolecules by adopting the overall model; according to the method, the problems of structure and semantic segmentation, weak knowledge migration and poor model generalization ability in a molecular toxicity detection model in the prior art are solved.