Abstract: A context-based data processing tool is provided to facilitate data processing within a computing environment, where executing the context-based data processing tool includes capturing, by at least one processor set, selected content data from an electronic media, and generating, via artificial intelligence, relevant context metadata for the captured content data, and associating the generated context metadata with the captured content data. Further, executing the data processing tool includes integrating, by the at least one processor set, the captured content data into a context-based data store using the generated context metadata, where the generated context metadata facilitates processing of the captured content data within the computing environment.

Abstract: Aspects of the disclosure include methods and systems for content moderation, and specifically dynamic multimodal prompt generation for efficient content moderation. A method includes receiving, by a prompt generation system, a request for a decision corresponding to content. The method includes generating, by an encoder of the prompt generation system, an embedding of the content, and retrieving, by an embedding based retrieval (EBR) module of the prompt generation system, K retrieved chunks from a database, the K retrieved chunks having a Kth closest distance to the embedding in an embedding space. A dynamic prompt comprising a prompt template, multiple retrieved chunks of the K retrieved chunks, and the content is generated and input to a pre-trained large language model. The LLM generates the decision, which is returned responsive to the request.

Abstract: Systems and techniques for are described herein. A content passage is received from a corpus of training data comprising labeled training data and unlabeled training data. A query is received from a query hierarchy for a classification domain. The content passage and the query are embedded to form a passage-query pair. A predicted result for the passage-query pair is generated based on a calculated probability of the predicted result being within an answer threshold. A passage-query-result triplet is generated that comprises the passage-query pair and the predicted result according to the query hierarchy for the classification domain. Vectors of the content classification large language model are updated using the passage-query-result triplet.

Abstract: Systems and techniques for are described herein. A content passage is received from a corpus of training data comprising labeled training data and unlabeled training data. A query is received from a query hierarchy for a classification domain. The content passage and the query are embedded to form a passage-query pair. A predicted result for the passage-query pair is generated based on a calculated probability of the predicted result being within an answer threshold. A passage-query-result triplet is generated that comprises the passage-query pair and the predicted result according to the query hierarchy for the classification domain. Vectors of the content classification large language model are updated using the passage-query-result triplet.