Abstract: A computer-implemented method for obtaining actionable application feedback includes executing an application on remote computing systems and, during execution of the application on each computing system: surfacing a UI including a survey UI element on the display of the computing system; surfacing a first survey prompt via the survey UI element; receiving user input including a response to the first survey prompt, inputting the user input to an LLM; generating, via the LLM, a second survey prompt based on the provided user input; surfacing the second survey prompt via the survey UI element; receiving user input including a verbatim response to the second survey prompt; and generating, via the LLM, topic tag(s) corresponding to the verbatim response. The method includes aggregating the verbatim responses received via the computing systems according to the corresponding topic tag(s), as well as generating, via the LLM, application insights based on the aggregated verbatim responses.

Abstract: Systems and methods are directed to building annotated models based on eyes-off data. Specifically, a synthetic data generation model is trained and used to further train a target model. The synthetic data generation model is trained within an eyes-off environment using an anonymity technique on confidential data. The synthetic data generation model is then used to create synthetic data that closely represents the confidential data but without any specific details that can be linked back to the confidential data. The synthetic data is then annotated and used to train the target model within an eyes-on environment. Subsequently, the target model is deployed back within the eyes-off environment to classify the confidential data.

Abstract: Systems and methods are directed to building annotated models based on eyes-off data. Specifically, a synthetic data generation model is trained and used to further train a target model. The synthetic data generation model is trained within an eyes-off environment using an anonymity technique on confidential data. The synthetic data generation model is then used to create synthetic data that closely represents the confidential data but without any specific details that can be linked back to the confidential data. The synthetic data is then annotated and used to train the target model within an eyes-on environment. Subsequently, the target model is deployed back within the eyes-off environment to classify the confidential data.

Abstract: A system and method automatically generating a title for a cluster of documents includes accessing a plurality of documents that have been categorized as belonging to a document cluster and providing the plurality of documents as an input to a trained title generating machine-learning (ML) model. The trained title generating ML model is trained for generating a title for a document and provides a titles for each of the plurality of documents. An embedding is created for the generated titles and then an embedding is generated for the document cluster. A similarity between the embeddings for the titles and embedding for the document cluster is measured to identify titles that are more similar to the embedding for the document cluster and based on the similarity one or more titles are selected as title candidates for the document cluster and provided as an output.