Abstract: Systems and methods for using an artificial intelligence-based technique for automatic generation of travel and dining reviews are provided. The method includes: receiving a request for a review of an entity that provides a service to a user; applying a first artificial intelligence (AI) algorithm to the received request in order to generate the review of the entity; and outputting the review of the entity. The entity provides either or both of a travel-related service and a dining-related service, and as such, the entity may include a restaurant or a hotel. The AI algorithm may use a large language model and/or may be trained such that the review has a style and a tone of a Zagat review.

Abstract: Methods and systems for detecting and mitigating hallucinations in artificial intelligence (AI) summarizations of user-generated content are provided. The method includes: receiving an AI-generated content item; retrieving historical content items that have been generated by human beings; comparing the AI-generated content item with the historical content items in order to determine whether text string matches are present; when a determination is made that no match exists, performing a semantic matching operation to identify text strings included in the historical content items that are semantically similar to text strings in the AI-generated content item; and determining, based on the comparison and the semantic matching operation, whether the AI-generated content item is a hallucination. When a hallucination is detected, the hallucination may be mitigated by removing a textual perturbation and/or replacing the textual perturbation with text that accurately reflects the original content item.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, as input to a binary search process, a subject vector embedding and a class vector embedding, wherein the subject vector embedding is generated from a plurality of subject text strings and wherein the class vector embedding is generated from a class text string; generating a similarity score; determining that the similarity score is below a threshold value; splitting the plurality of subject text strings into a first new plurality of subject text strings and a second new plurality of subject text strings; receiving a new subject vector embedding, wherein the new subject vector embedding is generated from the first new plurality of subject text strings; and calling the binary search process recursively using the new subject vector embedding and the class vector embedding as input to the binary search process.

Abstract: A method for performing differential routing based on message content. The method includes receiving, from a user device, a message inquiry; identifying and extracting one or more keywords from the message inquiry; determining, for each of the one or more keywords, a corresponding tag type; predicting a conversation length based on the determined tag type for each of the one or more keywords; determining whether the message inquiry is amenable to being serviced by an automated service; and routing the message inquiry based on the determining whether the message inquiry is amenable to being serviced by the automated service.

Abstract: Systems and methods for using an artificial intelligence-based technique for automatic generation of travel and dining reviews are provided. The method includes: receiving a request for a review of an entity that provides a service to a user; applying a first artificial intelligence (AI) algorithm to the received request in order to generate the review of the entity; and outputting the review of the entity. The entity provides either or both of a travel-related service and a dining-related service, and as such, the entity may include a restaurant or a hotel. The AI algorithm may use a large language model and/or may be trained such that the review has a style and a tone of a Zagat review.

Abstract: A method for using artificial intelligence and machine learning to automatically generate commentary that relates to quantitative data is provided. The method includes: receiving a first tabular data set; identifying a first cell having numeric data from within the first tabular data set; associating a first column header and a first row header with the first cell; assigning a respective header type to each of the first column header and the first row header; mapping each of the first cell, the first column header, and the first row header to a first comment from among a set of comments; and augmenting the first comment by generating additional text that supplements the first comment.

Abstract: A method for performing differential routing based on message content. The method includes receiving, from a user device, a message inquiry; identifying and extracting one or more keywords from the message inquiry; determining, for each of the one or more keywords, a corresponding tag type; predicting a conversation length based on the determined tag type for each of the one or more keywords; determining whether the message inquiry is amenable to being serviced by an automated service; and routing the message inquiry based on the determining whether the message inquiry is amenable to being serviced by the automated service.

Abstract: A method for using artificial intelligence and machine learning to automatically generate commentary that relates to quantitative data is provided. The method includes: receiving a first tabular data set; identifying a first cell having numeric data from within the first tabular data set; associating a first column header and a first row header with the first cell; assigning a respective header type to each of the first column header and the first row header; mapping each of the first cell, the first column header, and the first row header to a first comment from among a set of comments; and augmenting the first comment by generating additional text that supplements the first comment.

Abstract: Systems and methods for digital document generation using natural language interaction are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for digital document generation using natural language interaction may include: (1) receiving from an electronic device, a natural language command comprising an action to generate digital content, an object, and a data source for the digital content; (2) processing the natural language command to identify the action, the type of digital content, and the data source; (3) identifying a skill in a skill library that is mapped to the action and the object; (4) retrieving data from the data source for the skill; and (5) generating the digital content according to the skill using the data.