Abstract: A computer-implemented method comprising: receiving a source dataset comprising a plurality of textual data instances and corresponding labels in two or more classes; training a machine learning classifier on the source dataset; performing inference by the trained machine learning classifier over a subset of the data instances in the source dataset, to extract a hidden representation for each of said data instances in said subset; applying a trained multilayer perceptron (MLP) network to the extracted hidden representations, to generate a set of corresponding soft prompts; and feeding the generated set of soft-prompts as prompts for a trained language model, to tune the trained language model to reconstruct the data instances in the subset.

Abstract: Mechanisms are provided for enhancing an Application Programming Interface (API) specification. The mechanisms receive an existing API specification and an API document that describes the API, and identifies an element candidate in the API document based on a matching of elements in the existing API specification with elements in the API document. The mechanisms determine, for the element candidate, a minimal ancestor based on one or more predetermined minimal ancestor criteria. In addition, the mechanisms generate additional content for the existing API specification which specifies parameter metadata for the element candidate, based on an application of an artificial intelligence (AI) language model (LM) to the minimal ancestor. Moreover, the mechanisms integrate the additional content into the existing API specification to thereby generate an enhanced API specification.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to predictive tool sequencing based on context awareness. In this regard, a system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise an access component that can access a context window comprising a sequence of application programming interface (API) tools. The computer executable components can further comprise a prediction component that can predict an API tool for a defined position within the context window, based on a context provided by the sequence of API tools.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a hybrid language model with an encoder model and graph search algorithm for planning problems. For example, a system can comprise a memory that can store computer executable components and a processor that executes at least one of the computer executable components that can receive a current state, a set of applicable actions, and a set of goal states that define a planning problem. The at least one of the computer executable components can further generate, via a language model, a plan for the planning problem, wherein generating the plan for the planning problem can comprise: generating, via an encoder, respective encoded representations of the current state, the set of applicable actions and evaluating, via a graph search algorithm, a set of plans based on the encoded representations.

Abstract: A computer-implemented method comprising: receiving a source dataset comprising a plurality of textual data instances and corresponding labels in two or more classes; training a machine learning classifier on the source dataset; performing inference by the trained machine learning classifier over a subset of the data instances in the source dataset, to extract a hidden representation for each of said data instances in said subset; applying a trained multilayer perceptron (MLP) network to the extracted hidden representations, to generate a set of corresponding soft prompts; and feeding the generated set of soft-prompts as prompts for a trained language model, to tune the trained language model to reconstruct the data instances in the subset.

Abstract: An example system includes a processor to receive data for a multi-objective task. The processor is to also perform the multi-objective task on the received data via a trained primal network. The primal network and a dual network are trained for a multi-objective task using a Lagrangian loss function representing a number of objectives. The primal network is trained to minimize the Lagrangian loss function and the dual network is trained to maximize the Lagrangian loss function.

Abstract: An example system includes a processor to receive a prefix of conversation and a text input. The processor is to also generate a completed response based on the prefix of conversation and the text input via a trained primal network. The primal network is trained to minimize a Lagrangian loss function representing a number of objectives and a dual network is trained to maximize the Lagrangian loss function.

Abstract: A method, computer system, and a computer program product for enhanced rating predictions is provided. The present invention may include receiving a user input. The present invention may then include translating the received user input into an embedding matrix and inputting the embedding matrix into a deep neural network. The present invention may further include generating, by the deep neural network, an output vector, a user bias term and an item bias term based on the embedding matrix. The present invention may then include calculating a predicted rating based on the generated output vector, the generated user bias term and the generated item bias term. The present invention may then include determining an accuracy of the predicted rating.

Abstract: An example system includes a processor to receive a prefix of conversation and a text input. The processor is to also generate a completed response based on the prefix of conversation and the text input via a trained primal network. The primal network is trained to minimize a Lagrangian loss function representing a number of objectives and a dual network is trained to maximize the Lagrangian loss function.

Abstract: An example system includes a processor to receive data for a multi-objective task. The processor is to also perform the multi-objective task on the received data via a trained primal network. The primal network and a dual network are trained for a multi-objective task using a Lagrangian loss function representing a number of objectives. The primal network is trained to minimize the Lagrangian loss function and the dual network is trained to maximize the Lagrangian loss function.

Abstract: A method, computer system, and a computer program product for enhanced rating predictions is provided. The present invention may include receiving a user input. The present invention may then include translating the received user input into an embedding matrix and inputting the embedding matrix into a deep neural network. The present invention may further include generating, by the deep neural network, an output vector, a user bias term and an item bias term based on the embedding matrix. The present invention may then include calculating a predicted rating based on the generated output vector, the generated user bias term and the generated item bias term. The present invention may then include determining an accuracy of the predicted rating.