Abstract: Systems and methods are provided for an application programming interface (API) response compression system used in conjunction with API requests made by a large language model (LLM) agent in response to a prompt made to an LLM. The API response compression (ARC) system may receive an API response, generate a property manifest for the API response identifying a set of fields in the API response, generate a filtered property manifest identifying fields of the API response relevant to the prompt, generating a reduced API response, and processing the prompt and the reduced API response at the LLM to generate LLM output.

Abstract: Systems and methods provide for a multi-modal development environment to receive inputs using a variety of different input modalities in different user interfaces (UIs). Multiple user interfaces may be linked within the development environment to maintain state information so that inputs provided to one UI are represented in the other UIs using an appropriate equivalent representation based on the UI modality. Users of the development environment may select a given UI for interaction based on a desired task and then see changes tracked and relayed through the different UIs to verify changes within the development environment. The UIs may also be contextually linked to permit the user to work between both UIs without losing the context due to the switch.

Abstract: This patent application relates to using framework parameters with a large language model to create beams based on a prompt. The beams can be evaluated using multiple criteria of a reward model that can be weighted for importance. The beams can be evaluated according to each of the one or more criteria and compared to determine which beams most closely align with the criteria. The beam that best aligns can be selected to generate a response.

Abstract: Systems and methods provide for modular, iterative framework to resolve inputs provided to an interaction environment. The input may be decomposed into different component parts and then relevant actions for each of the component parts may be predicted. An action may be selected and relevant parameters may be populated based on the input. If parameters remain unresolved, additional queries may be presented in order to resolve the remaining parameters. Multiple actions may be executed and then prepared to generate a combined response responsive to the input. Actions for a given interaction environment may be domain-specific and also may be developer-defined for a given goal or task to restrict one or more underlying language systems.

Abstract: The application is directed to receiving an alignment model and a prompt to evaluate and generate a response using a large language model. The large language model populates tokens for potential responses based on framework parameters and evaluates the potential responses using an alignment model. The alignment model can be utilized or selected for a prompt at decoding-time and can be used to select a potential response to use to create an output.

Abstract: Systems and methods provide for a multi-modal development environment to receive inputs using a variety of different input modalities in different user interfaces (UIs). Multiple user interfaces may be linked within the development environment to maintain state information so that inputs provided to one UI are represented in the other UIs using an appropriate equivalent representation based on the UI modality. Users of the development environment may select a given UI for interaction based on a desired task and then see changes tracked and relayed through the different UIs to verify changes within the development environment. The UIs may also be contextually linked to permit the user to work between both UIs without losing the context due to the switch.

Abstract: A natural language processing system may implement one or more composite slot types. The natural language processing system may receive a request to create a composite parameter type for causing performance by the natural language processing system of an intended action. The natural language processing system determines based on the request, a plurality of sub-parameters of the composite parameter type and generates a Boolean expression that relates two or more sub-parameters. The natural language processing system may apply the composite parameter type to determine that the pluralities of sub-parameters in an input text satisfies the Boolean expression to initiate performance of the intended action.

Abstract: Techniques are described for a contextual natural language understanding (cNLU) framework that is able to incorporate contextual signals of variable history length to perform joint intent classification (IC) and slot labeling (SL) tasks. A user utterance provided by a user within a multi-turn chat dialog between the user and a conversational agent is received. The user utterance and contextual information associated with one or more previous turns of the multi-turn chat dialog is provided to a machine learning (ML) model. An intent classification and one or more slot labels for the user utterance are then obtained from the ML model. The cNLU framework described herein thus uses, in addition to a current utterance itself, various contextual signals as input to a model to generate IC and SL predictions for each utterance of a multi-turn chat dialog.

Abstract: A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.

Abstract: The present principles generally relate to multimedia processing and viewing, and particularly, to apparatuses and methods for obtaining enhanced user feedback ratings of a multimedia program. In an exemplary embodiment, viewers are provided with different opportunities to provide user feedback including feedback ratings for the multimedia program during various points in the playout of the program. The various points can be based on the content of the program or during different selected time intervals during the playout of the program.

Abstract: A receiver on a mobile device accepts at least one unrequested signal from a transmitter located in proximity of a first screen showing a first multimedia content. The received signal contains information associated with a second multimedia content shown on a second screen. The information can trigger an application residing on the mobile device based on the characteristics of the received signal. The information is accepted by the application which then generates second screen information associated with a displaying of a second multimedia content on a second screen, the generation of the second screen information based upon the information, reception of the transmitted signal and a location of the second screen.