Abstract: Some aspects relate to technologies for dynamically generating digital content for events using event data and content intent descriptors. In some aspects, when a content server identifies an event for digital content creation, the content server provides data to a user device that is based on event data for the event and a content intent descriptor. The user device generates a prompt using the received data and provides the prompt to a generative model on the user device, causing the generative model to generate a digital content item using the prompt as input. The user device then presents the digital content item.

Abstract: learned from digital content items and their corresponding performance metrics. In accordance with some aspects, a training dataset is accessed that comprises training samples that each include a digital content item and a performance metric, and the multimodal generative model is trained using the training data. The training can include, for a training sample, using encoders of the multimodal generative model to generate a latent representation of a digital content item from the training sample and a latent representation of a performance metric from the training sample. The latent representations are merged to provide a combined latent representation, and decoders of the multimodal generative model decode the combined latent representation to provide an output digital content item and output performance metric. Losses are determined from the outputs and used to update parameters of the multimodal generative model.

Abstract: Some aspects relate to technologies for generating custom digital content using a content intent descriptor from a content server and on-device contextual data maintained on a user device. In some aspects, a user device receives a content intent descriptor communicated over a network from a content server. The user device generates a prompt using the content intent descriptor and on-device contextual data maintained on the user device. A generative model is caused to generate a digital content item using the prompt, and the digital content item is presented on the user device.

Abstract: Some aspects relate to technologies for generating and/or presenting digital content using on-device subscription data maintained on a user device. In some aspects, a user device receives a content intent descriptor communicated over a network from a content server. The user device performs a comparison of on-device subscription data with the content intent descriptor. Based on the comparison, the user device generates a prompt using the content intent descriptor. A generative model is caused to generate a digital content item using the prompt, and the digital content item is presented on the user device.

Abstract: Some aspects relate to technologies for performance-guided content generation and exploration using a multimodal generative model with a joint latent space learned from digital content items and their corresponding performance metrics. In accordance with some aspects, input is received for content generation. The input includes a digital content item and is encoded by one or more encoders of the multimodal generative model into a latent representation in the joint latent space. A latent space transformation from the latent representation of the input is performed to provide a transformed latent representation, which is decoded by one or more decoders of the multimodal generative model to generate an output digital content item. In some aspects, the one or more decoders also decode the transformed latent representation to generate a predicted performance metric for the output digital content item.

Abstract: A method, non-transitory computer readable medium, system, and apparatus for data processing includes obtaining a user query and a plurality of context examples and generating a first input and a second input. The first input comprises the user query appended to a first portion of the plurality of context examples, and the second input comprises the user query appended to a second portion of the plurality of context examples. The method, non-transitory computer readable medium, system, and apparatus for data processing further includes generating a response to the user query based on the first input and the second input.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting specific ambiguity types in queries to a large language model. The disclosed system determines a query in a prompt by a client device to a large language model. The disclosed system generates, utilizing a small language model, a label indicating that the query comprises an ambiguity of an identified ambiguity type of a plurality of ambiguity types according to a plurality of quantitative features of the query. Additionally, the disclosed system generates, for display to the client device, a response to the query based on the ambiguity of the identified ambiguity type.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating modifications to an LLM based artificial intelligence assistant based on classifying the severity of errors and focusing the modifications on resolving high-severity errors. In particular, the disclosed systems receive prompts via an artificial intelligence assistant graphical user interface and generate responses to the prompts using the LLM based artificial intelligence assistant. Further, the disclosed systems determine errors in the responses using an annotation tool to generate annotated errors and an error analysis mechanism to generate indications of the errors based on the annotated errors. Additionally, the disclosed systems classify the errors as one of high-severity, mid-severity, or low-severity. Moreover, the disclosed systems generate modifications to components of the LLM based artificial intelligence assistant based on the high-severity errors.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize collaborative filtering and a reinforcement learning model having an actor-critic framework to provide digital content items across client devices. In particular, in one or more embodiments, the disclosed systems monitor interactions of a client device with one or more digital content items to generate item embeddings (e.g., utilizing a collaborative filtering model). The disclosed systems further utilize a reinforcement learning model to generate a recommendation (e.g., determine one or more additional digital content items to provide to the client device) based on the user interactions. In some implementations, the disclosed systems utilize the reinforcement learning model to analyze every negative and positive interaction observed when generating the recommendation.

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that intelligently generate and modify schedules of task sequences utilizing a graph neural network and/or reinforcement learning model. For example, the disclosed system utilizes a graph neural network to generate performance efficiency scores indicating predicted performances of the sets of tasks. Additionally, the disclosed systems utilizes the performance efficiency scores to rank sets of tasks and then determine a schedule including an ordered sequence of tasks. Furthermore, disclosed system generates modified schedules in response to detecting a modification to the schedule. For example, the disclosed system utilizes a reinforcement learning model to provide recommendations of new tasks or task sequences deviating from the schedule in the event of an interruption. The disclosed system also utilizes the reinforcement learning model to learn from user choices to inform future scheduling of tasks.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for data processing include receiving a natural language query including a request for data from a database, generating a natural language query embedding representing the natural language query in a vector space, and determining a validity of the natural language query by comparing the natural language query embedding to a valid query embedding in the vector space. Some embodiments include converting the natural language query into a structured query based on the validity of the natural language query and retrieving the data from the database using the structured query.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating action plans utilizing a large language model with a best-first search model. The disclosed system determines a request to utilize a large language model to generate an action plan via one or more software tools. The disclosed system generates the action plan by traversing a decision tree comprising an action space involving the one or more software tools by iteratively: selecting, utilizing a best-first search model, an action from a set of possible actions in the action space of the decision tree; and expanding, utilizing the best-first search model, the action space of the decision tree to include an additional set of possible actions. The disclosed system also executes the action plan via one or more interactions with the one or more software tools according to the action.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

Abstract: In some embodiments, techniques for identifying email events generated by bot activity are provided. For example, a process may involve applying bot detection patterns to identify bot activity among email response events.

Abstract: Systems and methods for dynamic user profile management are provided. One aspect of the systems and methods includes receiving, by a lookup component, a request for a user profile; computing, by a profile component, a time-to-live (TTL) refresh value for the user profile based on a lookup history of the user profile; updating, by the profile component, a TTL value of the user profile based on the request and the TTL refresh value; storing, by the profile component, the user profile and the updated TTL value in the edge database; and removing, by the edge database, the user profile from the edge database based on the updated TTL value.

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

Abstract: Methods and systems are provided for using generative artificial intelligence to evaluate fine-tuned language models. In embodiments described herein, natural language text snippets are generated via a generative language model based on corresponding data. A language model is fine-tuned into a fine-tuned language model via a language model fine-tuning component using the natural language text snippets and the corresponding data as training data. Independent natural language text snippets are generated via the generative language model based on the corresponding data. Each independent natural language text snippet is different than each corresponding natural language text snippet. An evaluation metric of the fine-tuned language model is generated via an evaluation component based on the independent natural language text snippets and the corresponding data.

Abstract: An edge node included in a decentralized edge computing network generates a federated partial-data aggregation machine learning model. The edge node learns one or more model parameters via machine learning techniques and receives one or more auxiliary model parameters from additional edge nodes in the decentralized edge computing network, such as from a neighbor node group. In some cases, a neighbor node is identified in response to determining that the neighbor node includes a model with a relatively high estimated relevance to the model of the edge node. The edge node modifies the model to include an aggregation of the learned model parameters and the received auxiliary parameters. Respective weights are learned for the learned model parameters and also for the received auxiliary parameters. During training to learn the respective weights, the edge node stabilizes the learned model parameters and the received auxiliary parameters.

Abstract: Various disclosed embodiments are directed to using one or more algorithms or models to select a suitable or optimal variation, among multiple variations, of a given content item based on feedback. Such feedback guides the algorithm or model to arrive at suitable variation result such that the variation result is produced as the output for consumption by users. Further, various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things, as described in more detail below.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.