10 results about "Context independent" patented technology

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a cue word compiling and generating method and system for a large language model.The method comprises the steps that in response to a compiling calling request, a PDL protocol is received to serve as a compiling input text; performing lexical and grammatical analysis on the compiled input text, and constructing a corresponding abstract syntax tree AST as an intermediate representation according to a context-independent method; semantic verification and structure correction are carried out on the AST, and when semantics are effective, structure optimization is carried out to generate an optimized AST; and traversing the optimized AST, matching a cue word template in a cue word generation rule base, mapping semantic nodes into structured text fragments, splicing the structured text fragments to form a final cue word text, and outputting a compilation result consistent with PDL protocol semantics. By introducing a PDL-oriented compiling mechanism, automatic analysis, structure optimization and high-quality cue word generation of an input protocol are realized before cue words of a large language model are generated, and the development efficiency and the output quality of the cue words are improved.

System, method and interface for generating data visualizations are provided. The system receives a user input to specify a natural language command directed to a data source. The system also generates a prompt for generating a data visualization based on relevant data fields and data values, rules that characterize the data visualization, and a context free grammar. The system also prompts a trained large language model using the prompt to generate a structured document following a domain-specific schema based on a shorthand notation. The system also uses a parser that uses the context free grammar to map the structured document to a visual specification. The visual specification specifies the data source, visual variables, and data fields from the data source. The system also generates and displaying a data visualization based on the visual specification, including displaying visual marks representing data, retrieved from the data source, for the data fields.

The invention provides a method for acquiring a general Chinese emotion event based on a large language model, and relates to the technical field of natural language processing. According to the method, the language features of the Chinese sentiment events are analyzed, the sentiment event identifiers are systematically constructed, the prompt engineering is adopted to guide the large model to generate the general Chinese sentiment events irrelevant to the context, and the problem that the general Chinese sentiment events are difficult to obtain through a traditional method is solved. Language features of Chinese emotional events are mined, Chinese emotional event identifiers are firstly obtained, and then positive event identifiers and negative event identifiers are utilized to construct prompt guidance large models to generate general emotional events, so that the quality and the number of the obtained emotional events are improved. According to the method, large-scale high-quality general emotion events can be obtained, and the performance of the emotion reason extraction model is improved.

The invention discloses an energy consumption prediction method and system based on a multi-modal large model and context awareness, and the method comprises the steps: firstly collecting multi-modal time series data of an energy system, then generating word vector representation in a large language model, and reserving key words related to an energy consumption prediction task through correlation analysis; and performing fusion processing on the environment sequence, and generating an energy consumption prediction result through a context-independent prediction model and a prediction branch of a large language model according to the environment sequence and a historical energy consumption sequence. According to the method, the entanglement problem during time sequence and text information fusion can be solved, effective fusion of multi-modal context information is realized, and the accuracy of energy consumption prediction is improved.

This invention relates to the fields of artificial intelligence and natural language processing, and particularly to a method and system for compiling and generating prompt words for large language models. The method includes responding to a compilation call request and receiving a PDL specification as the compilation input text; performing lexical and syntactic analysis on the compilation input text, and constructing a corresponding Abstract Syntax Tree (AST) as an intermediate representation using a context-free method; performing semantic verification and structural correction on the AST, and performing structural optimization when the semantics are valid to generate an optimized AST; traversing the optimized AST, matching prompt word templates in the prompt word generation rule base, mapping semantic nodes to structured text fragments, concatenating the structured text fragments to form the final prompt word text, and outputting a compilation result consistent with the semantics of the PDL specification. By introducing a PDL-oriented compilation mechanism, the method achieves automated parsing of the input specification, structural optimization, and high-quality prompt word generation before prompt word generation for large language models, improving the efficiency of prompt word development and output quality.

Methods, systems, and computer storage media for providing an AI-supported fraud detection engine in an item listing system are described. The M-supported fraud detection engine leverages language models (e. g., LLMs) to interpret unstructured data as language model output (i. e., language model output comprising context-free annotation tags including observations, tags, category, case summary, and reasoning annotations). The language model output (i. e., context-free annotation tags) is mapped to context-aware target tags without explicit tagging (i. e., zero-shot approach) to generate structured domain-specific tags. The zero-shot approach allows the AI-supported fraud detection engine to recognize and categorize new fraud pattems dynamically. The structured domain-specific tags can be integrated into traditional fraud detection models that leverage the insights from the structured domain-specific tags to generate fraud detection recommendations. The Al-supported fraud detection engine's ability to generate fraud detection recommendations based on structured domain-specific tags leads to more targeted and accurate responses to potential fraud cases.

Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to provide one or more indicators of context information corresponding to one or more kernels. The application programming interface can be used to provide indicators of context information for context-free kernels and context.

The application discloses a visual language model hallucination relieving method based on attention guidance and belongs to the technical field of multimodal reasoning and natural language processing. In view of the problem that a visual language model is insufficient in learning visual information and thus hallucination is caused, the application provides a visual language model hallucination relieving method based on attention guidance. By calculating attention scores, visual tokens irrelevant to the context and related visual tokens in the decoding process are recognized, and the attention weights of the two types of visual tokens are adjusted, so that the model pays more attention to the related visual area, the visual fidelity is enhanced, and the hallucination is relieved.

The invention provides a programming language processing method and device and electronic equipment. The method comprises the steps that a programming language is acquired; the programming language is obtained through conversion based on a visual node flow chart edited by a user; performing field detection and conversion on the programming language to obtain a target character string; the field corresponds to a grammar structure name in a predefined context-independent grammar rule; inputting the target character string into a preset push-down automaton, so that the push-down automaton checks whether the target character string accords with a context-independent grammar rule or not; the push-down automaton is obtained through conversion based on a context-independent grammar rule; and performing subsequent processing based on the verification result. A structured programming language is converted into a specific character string to be input, a push-down automaton obtained through context-independent grammar conversion is used for performing pre-operation verification, and the prior mode can solve the problem that large-granularity input is illegal.