Embodiments described herein provide a method of detecting whether an input text is AI-generated using a neural network based language model. The method may include: formulating a span detection prompt including the input text and examples of problematic texts; generating, using the neural network based large language model in response to the span detection prompt, a textual spans in the input text and a category for each textual span in the plurality of textual spans; formulating an edit category prompt including the plurality of textual spans, the category for each textual span, and a plurality of example edits for each category; generating, using the neural network based large language model in response to the edit category prompt, a plurality of edited textual spans associated with the category; generating a revised sample text from the edits of the plurality of textual spans; and outputting, to a display, the revised sample text.

A method for generating a response to a user query includes receiving a user query that involves user query text and/or one or more user query images. The method also includes converting the user query into a contextualized query using a multimodal retrieval-augmented generation (RAG) agent. The method also includes retrieving paths from a vector database in response to the contextualized query. The method also includes retrieving contents from a storage in response to the paths. The method also includes generating an answer to the user query based upon the contents.

A target prediction method for predicting a future outlook of a target performed by a computing device or a processor may collect related structured and unstructured data when a user requests predictive generation, analyze the relationship between the target and a variable affecting the target at a semantic level, and compute a target outlook of a future.

A question-answering method may comprise determining a focus object in response to a predefined response-requesting shortcut input, displaying a prompt input window adjacent to the focus object, and displaying a response display window at an input position of the prompt input window for a query input into the prompt input window, wherein a response displayed in the response display window is a response generated by a generative artificial intelligence (AI) service using the focus object and the query.

Described is a system that receives data from a variety of external data repositories and identifies unstructured data within the received content. The unstructured data is processed to generate textual representations. A chat message is displayed in a user interface, prompting the first user to submit a query. Upon receiving the user's query, the system generates a modified version of the query and identifies portions of the textual representations. A content block is then generated from these portions and input into a machine learning model trained to generate responses using content blocks. The system generates a response to the user's query and displays the response within the user interface.

The present disclosure provides a method for generating document question-answering data, a training method, a generating apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The method includes: extracting page content from page images in a document to obtain descriptive information corresponding to seed pages in the document; generating a reasoning chain corresponding to the seed pages by using a preset question-answering data generation model based on the descriptive information, question definitions of preset question types, and question-answering examples of the preset question types; and in response to the reasoning chain constituting a question-type reasoning chain corresponding to the preset question types, generating question-answering data corresponding to the preset question types by using the question-answering data generation model based on the question-type reasoning chain.

Some techniques relate to generating a knowledge graph including textual passages and textual summaries usable for producing an answer to a question relating to a domain. A corpus of textual information is received. Textual passages and descriptions of associations between textual passages from a first language model are obtained by providing at least a portion of the corpus of textual information to the first language model. Textual summaries corresponding to textual passages are obtained from a second language model. A knowledge graph is generated based on the textual passages, descriptions of associations between the textual passages, and textual summaries. The knowledge graph is stored in a non-transitory computer readable medium.

Systems, apparatuses, methods, and computer program products are disclosed for providing a multimodal interactive personal advisor (MIPA). An example method includes retrieving first user data associated with a user. The example method also includes facilitating an first interaction between the user and an MIPA model via a first user device. The example method also includes extracting, based on the first interaction, a set of data features associated with the user and determining, based on the set of data features, second user data. The example method also includes generating, based on the first user data and the second user data, a first model response and providing the first model response to the user via a second user device associated with the user.

A system for automated query resolution using a multi-agent processing framework is described. The system includes one or more processors coupled with memory to generate, using one or more machine learning models and based on a query associated with an account, an embedding corresponding to a vector representation of the query. The system can perform, using the embedding, a vector semantic search in a vector space of a plurality of queries to identify documentation associated with the query and a matching question response pair. The system can identify, from a knowledge graph using the documentation and metadata, one or more entities related to the question response pair and one or more relationships between the entities. The system can select, from a plurality of agents, an agent to provide an interaction with the user to address the entities and relationships, and provide, via the processing framework, a response of the query response pair responsive to the interaction.

A method includes: obtaining a document comprising at least one page for question answering; determining a first vector corresponding to each of the at least one page; determining a second vector corresponding to a target question text to be answered; performing the following first operations: determining, based on the second vector and the first vector corresponding to each of the at least one page, a first similarity between the target question text and each of the at least one page; determining, based on the first similarities, at least one candidate page with the highest similarity to the target question text among the at least one page; and generating, based on the at least one candidate page and the target question text, a first identifier and first content, or second identifier and second content, using a large language model.