Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate capturing relative importance of relational entities for building database embedding models are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components can comprise a template component that utilized natural language as a prompt template to describe a perspective of clustering and assembles description information into the prompt template to generate a base model. The computer executable components can comprise a training component that can utilize data in the prompt template to automatically build training data of an adapter to generate a final model. The computer executable components can comprise a vector generator component that inputs the prompt template to the final model to generate one or more hidden layer vectors highlighting characteristics of the natural language.

Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: An embodiment for a method of extracting information from documents using knowledge graphs and prompt-based learning. The embodiment may receive a document and perform optical character recognition (OCR) to obtain OCR text lines and associated bounding boxes. The embodiment may encode each of the obtained OCR text lines into semantic vectors and each of the associated bounding boxes into position vectors to generate a knowledge graph using fusion vectors derived therefrom. The embodiment may receive a query including a key value. The embodiment may identify a series of candidate nodes including a series of most similar nearby nodes positioned near a first node associated with the key value. The embodiment may generate prompt template to determine closeness of the candidate nodes to the key value and calculate associated confidence levels. The embodiment may output extraction information associated with the candidate node having a highest calculated confidence level.