1745results about "Text database querying" patented technology

The invention relates to the field of data processing, in particular to a memory retrieval method based on a large language model and a related device. The method comprises the following steps: in response to an interaction instruction sent by a target user, performing context analysis processing on the interaction instruction to obtain a real-time interaction intention of the target user; performing memory retrieval on a short-term memory module and a long-term memory module based on the real-time interaction intention to obtain target retrieval information; and in combination with a personalized knowledge base of the target user, carrying out personalized processing on the target retrieval information to obtain response information matched with the use habit of the target user, and using the response information to respond to the interaction instruction. According to the method, the memory capacity of the large language model in the interaction process is improved, the information forgetting rate is reduced, the information retrieval efficiency is improved, effective classification and organization of personalized information are achieved, and the user experience is improved.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifies corresponding blocks of text using an content extraction service. A prompt is generated using the blocks of text and used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

This disclosure relates to methods, systems, and devices for AI / ML assisted management of a radio access network (RAN). In particular, an LLM is employed to generate answer to user queries, for example in the form of code that can be executed upon streaming data from the RAN, including performance management and control management databases. To provide prompts the LLM can understand, an LLM agent employs a knowledge base and experience base to transform the language of the queries from the highly technical telecommunications domain to a general domain. Frequently asked questions and their answers may be stored in the experience base and used instead of using the LLM.

Provided in the embodiments of the present disclosure are a question answering processing method and system, a device and a storage medium. The method comprises: on the basis of a user question, a first prompt template containing first tool set information, and an LLM, a first agent acquires, successively generated by the LLM, a plurality of pieces of solving task information that are required to be executed by invoking a second agent and summary task information that is required to be executed by the first agent, and successively transmits the plurality of pieces of solving task information to the second agent; on the basis of received current solving task information, a second prompt template containing second tool set information, and the LLM, the second agent acquires task execution information that is generated by the LLM and corresponds to the current solving task information, and accordingly acquires from an application server a task execution result of the current solving task information; and the first agent summarizes the task execution results of the plurality of pieces of solving task information, so as to determine reply information corresponding to the user question. By means of the cooperation of different agents, user questions are gradually and accurately solved.

An index is created with split documents to retrieve and augment generation of a response to a natural language request using a generative machine learning model. When a natural language request is received, a search representation is generated and used to retrieve candidate portions of documents from the index. A relevancy ranking is performed to identify relevant portions of documents from the candidates and provide the relevant portions to prompt a generative machine learning model to provide a result for the natural language request.

This disclosure describes detecting hallucination as a metric for determining the accuracy of responses from a large language model (LLM). Scores with and without an augmented system are compared. The similarity or dissimilarity may be mapped into a hallucination score. The hallucination score can accurately predict when an answer is likely to be a hallucination. This is accomplished using similarity analysis on the text between un-altered responses and altered responses.

Examples for validating and optimizing responses generated by large language models (LLMs) are disclosed. A multi-pronged approach includes implementation of a logic programming engine that parses the responses into verifiable logic predicates to detect contradictions against a real-world knowledge base. The responses can further be weighed for category validation by using category theory to model the responses as mathematical morphisms to identify semantic inconsistencies that may be indicative of false information associated with the responses. The responses can be modified and supplemented in view of the validation operations described.

A method of automated technical support includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, the natural-language prompt including at least one technical support query, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, querying a second database using the vector embedding to retrieve second information, and generating a natural-language response text based on the natural-language prompt, the first information, and the second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, the second information comprises at least one text segment of the plurality of text segments, and the natural-language response text responsive to the at least one technical support query.

A system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and-Answer system with the possibility of extending to more complex generative AI interactions.

Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

A computing system including one or more processing devices configured to receive an image. The processing devices are further configured to compute a segmentation mask that identifies a region of interest included in the image. At a feature extractor, the processing devices are further configured to compute encoded image features based on the image. The processing devices are further configured to receive a text instruction. At a visual resampler, the processing devices are further configured to compute a mask query based on the segmentation mask, the encoded image features, and the text instruction. At a generative language model, the processing devices are further configured to receive a natural language query that includes the mask query and the text instruction. Based on the natural language query, at the generative language model, the processing devices are further configured to generate and output a semantic label associated with the region of interest.

A method for conversational query resolution includes receiving a query input from a user. The query input is decomposed into a plurality of tasks. A knowledge graph is queried to identify one or more relevant entities based on at least one of the plurality of tasks. A vector database is searched to identify one or more text chunks that correspond to the one or more relevant entities. Content relevant to at least one of the plurality of tasks is identified from the one or more text chunks. An answer is generated based on the identified content.

Various embodiments include systems and methods for generating a prompt for a generative artificial intelligence (AI) models. A processing system including at least one processor may be configured to recognize a user of the computing device, obtain user context information from a source of physical context information in the computing device, receive a user prompt for the large generative AI model (LXM), select a user profile from among a plurality of user profiles based on the user, the user context information and the user prompt, generate an enhanced prompt based on the user prompt and information included in the selected user profile, and submit the enhanced prompt to the LXM.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

A method of performing data analysis according to a natural language query from a user includes: receiving, from a user terminal, a user input including the natural language query requesting the data analysis; determining, based on the user input, at least one database among a plurality of databases as a target database; generating a prompt based on the user input and the target database; inputting the prompt into a code generation model to obtain a structured query (SQL) statement; executing the SQL statement to generate a result of the data analysis on the target database; and transmitting the result of the data analysis to the user terminal, where the result of the data analysis is displayed on a screen of the user terminal.

Natural language queries can be answered by using a large language model to find an appropriate user interface element appearing in an application. User interface element metadata can be incorporated when choosing the user interface element. Browser automation can then navigate to a page in the application on which the interface element appears, extract answer data, and then present the answer data as an answer to the natural language query. Input values can be supported, and a large language model can select a matching input value based on semantic matching, even if the natural language query does not have an exactly matching input value. Additional features such as pre-calculating embeddings, pre-determining candidate input values, and the like can be supported. The technologies can provide natural language access to web applications that can result in immediate access for new users and faster query execution by experienced users.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to enhancing LLMs using in-context learning and online knowledge. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a semantic retrieval component that can extract information from an online source, according to a query, to generate an in-context learning input utilized by an LLM for responding to the query.

Systems and methods for generating augmented search results are disclosed. An example method is performed by one or more processors of a search results ranking system and includes receiving a transmission over a communications network from a computing device associated with a user of the search results ranking system, the transmission including a search query, submitting, to a vector database, a token query matching a tokenized version of the search query against a plurality of data assets, submitting, to the vector database, one or more vector queries matching a vectorized version of the search query against the plurality of data assets, identifying, based on results of the token query and the one or more vector queries, contextually relevant results among the plurality of data assets, and generating augmented search results for the search query based on the contextually relevant results.

Systems and methods for generating a workflow data structure are provided. The system includes one or more processors; and one or more transitory or non-transitory computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising: receiving input data comprising a corpus of documents, a user query, and query context data; processing the corpus of documents to generate training data; training a large language model (LLM) using the training data; classifying, using the LLM, the user query to at least one content cluster of a plurality of content clusters based on the query context data; constructing, using the LLM, a workflow data structure as a function of the classifying; and generating, using the LLM, a query response as a function of the user query, the query context data, and the workflow data structure.

Applications of a language model are improved by creating a prompt that includes representations that improve the accuracy and reliability of the language model. The representations may be added to a prompt generated by a user. The representations are selected using embedding vectors of the representations and the user-generated prompt. Responses of the language model are selected by computing scores of the responses.

Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

The invention discloses an e-government affair platform management method and system based on cloud data, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, extracting keywords, carrying out the multi-scale time coding and multi-scale space coding of the data, and storing the data in a database; constructing a government affair knowledge graph based on the database, mining association relationships of department services and analyzing affair handling habits of users; generating a government affair examination and approval path based on the knowledge graph, optimizing the government affair examination and approval path through reinforcement learning, dynamically adjusting the examination and approval process, performing a simulation test by using a digital twinning technology, and verifying the user satisfaction after the examination and approval process is optimized; and storing the government affair data in a database and performing authority management. According to the method, the electronic government affair data processing capability can be improved, the examination and approval efficiency is improved, the verifiability of an optimization strategy is enhanced, and technical support is provided for digital and intelligent transformation of government affair services.

Methods, systems, and devices for processing a natural language request are described. An identity management system may receive a user request for information maintained in the identity management system and related to a client organization. The request may be received in a natural language form. In response to the user request, a machine learning model may be employed to generate a query in a machine-readable language that is understandable by the identity management system. The model-generated query, when executed, may cause information responsive to the user's request to be retrieved and output for display. Based on receiving a selection of a portion of the information output for display, the machine learning model may be employed to generate a natural language explanation of the selected portion. In some cases, the natural language explanation may be a summarization of information associated with the selected portion and retrieved from multiple data sources.

A system is provided for processing user queries by using an automated agent and a workflow. The system comprises reusable components that include states, tools, and / or data sources. Based on analysis of a query's content and goals, the system generates a workflow comprising a sequence of states, each state optimized for a subtask and dynamically bound to a selected tool(s) for that specific query. The workflow can provide a structured high-level control, while allowing for flexible selection of the tool(s) for each state of the workflow for that given query. The system produces a result using the structured workflow and selected tools, answering a user's original query.

Systems or techniques are provided for facilitating retrieval augmented generative question and answer boosting. In various embodiments, a system can access a plain text question regarding a scientific instrument. In various aspects, the system can generate, via a large language model that references a document-graph repository, a structured or unstructured answer for the plain text question. In various instances, the document-graph repository can comprise a plurality of document-graphs that respectively correspond to a plurality of technical documents. In various cases, for a first document-graph that corresponds to a first technical document, leaf nodes of the first document-graph can represent respective text blocks written in the first technical document, and non-leaf nodes of the first document-graph can respectively represent a document title, one or more section headings, and one or more scientific instrument identifiers written in the first technical document and beneath which the respective text blocks are nested.

The present invention relates to the technical field of large models, and relates to a general text quality evaluation method based on a large language model. The method comprises the following steps: 1) using a large language model to construct a general text quality evaluation model; 2) constructing training data, the training data having inputs of a prompt word and an evaluation input and an output of an evaluation result, the prompt word comprising an instruction, an evaluation rule, and an output format, and the constructing the training data comprising constructing training data containing reference answers and constructing training data containing no reference answers; 3) using the training data to train the general text quality evaluation model; and 4) using the trained text quality evaluation model to perform quality evaluation on a general text. The present invention can obtain high-quality training data containing reference answers and high-quality training data containing no reference answers at the same time, improving the data quality and performance under the setting containing no reference answers.

Implementations utilize a smaller LLM to generate content responsive to a user query and cause a portion of the generated content to be rendered as an immediate response to the user query. Implementations further utilize a larger LLM to generate content that starts with the portion of the generated content and that includes a refined portion succeeding the portion of the generated content. The refined portion can be rendered succeeding the portion of the generated content. In some implementations, instead of using the smaller LLM, alternatively, the portion of the generated content rendered as the immediate response can be generated based on a default text string or a template, where the template can be determined / selected from a plurality of predefined templates based on a natural language understanding of the user query.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

Distributed orchestration of data retrieval for generative machine learning model may be performed. When a natural language request to perform a natural language task is received that is associated with a generative application, one or more data retrievers may be selected to access associated data repositories according to a previously specified retrieval configuration for the generative natural language application. The data may then be obtained by the selected data retrievers and used to generate a prompt to a generative machine learning model. A result of the generative machine learning model may then be used to provide a response to the natural language request to perform the natural language task.